






"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A__					: Union[str, Any]=	{
    """configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
    """tokenization_roberta""": ["""RobertaTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__					: str=	["""RobertaTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__					: str=	[
      """ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """RobertaForCausalLM""",
      """RobertaForMaskedLM""",
      """RobertaForMultipleChoice""",
      """RobertaForQuestionAnswering""",
      """RobertaForSequenceClassification""",
      """RobertaForTokenClassification""",
      """RobertaModel""",
      """RobertaPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__					: int=	[
      """TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFRobertaForCausalLM""",
      """TFRobertaForMaskedLM""",
      """TFRobertaForMultipleChoice""",
      """TFRobertaForQuestionAnswering""",
      """TFRobertaForSequenceClassification""",
      """TFRobertaForTokenClassification""",
      """TFRobertaMainLayer""",
      """TFRobertaModel""",
      """TFRobertaPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__					: List[Any]=	[
      """FlaxRobertaForCausalLM""",
      """FlaxRobertaForMaskedLM""",
      """FlaxRobertaForMultipleChoice""",
      """FlaxRobertaForQuestionAnswering""",
      """FlaxRobertaForSequenceClassification""",
      """FlaxRobertaForTokenClassification""",
      """FlaxRobertaModel""",
      """FlaxRobertaPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
  from .tokenization_roberta import RobertaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_roberta_fast import RobertaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roberta import (
        ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        RobertaForCausalLM,
        RobertaForMaskedLM,
        RobertaForMultipleChoice,
        RobertaForQuestionAnswering,
        RobertaForSequenceClassification,
        RobertaForTokenClassification,
        RobertaModel,
        RobertaPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_roberta import (
        TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        TFRobertaForMultipleChoice,
        TFRobertaForQuestionAnswering,
        TFRobertaForSequenceClassification,
        TFRobertaForTokenClassification,
        TFRobertaMainLayer,
        TFRobertaModel,
        TFRobertaPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_roberta import (
        FlaxRobertaForCausalLM,
        FlaxRobertaForMaskedLM,
        FlaxRobertaForMultipleChoice,
        FlaxRobertaForQuestionAnswering,
        FlaxRobertaForSequenceClassification,
        FlaxRobertaForTokenClassification,
        FlaxRobertaModel,
        FlaxRobertaPreTrainedModel,
    )

else:
  import sys

  A__					: int=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)












"""simple docstring"""





A__					: Tuple=	"""Alexander Joslin"""

import operator as op

from .stack import Stack







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

    UpperCamelCase__         =    Stack()
    UpperCamelCase__         =    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
            UpperCamelCase__         =    operator_stack.peek()
            operator_stack.pop()
            UpperCamelCase__         =    operand_stack.peek()
            operand_stack.pop()
            UpperCamelCase__         =    operand_stack.peek()
            operand_stack.pop()

            UpperCamelCase__         =    operators[opr](SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
            operand_stack.push(SCREAMING_SNAKE_CASE					)

    # RULE 5
    return operand_stack.peek()


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




