




"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


SCREAMING_SNAKE_CASE    =	{
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	[
          "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
          "GPTNeoForCausalLM",
          "GPTNeoForQuestionAnswering",
          "GPTNeoForSequenceClassification",
          "GPTNeoForTokenClassification",
          "GPTNeoModel",
          "GPTNeoPreTrainedModel",
          "load_tf_weights_in_gpt_neo",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	[
          "FlaxGPTNeoForCausalLM",
          "FlaxGPTNeoModel",
          "FlaxGPTNeoPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_gpt_neo import (
                GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTNeoForCausalLM,
                GPTNeoForQuestionAnswering,
                GPTNeoForSequenceClassification,
                GPTNeoForTokenClassification,
                GPTNeoModel,
                GPTNeoPreTrainedModel,
                load_tf_weights_in_gpt_neo,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
      import sys

      SCREAMING_SNAKE_CASE    =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)











"""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)}')





