



'''simple docstring'''





from .balanced_parentheses import balanced_parentheses
from .stack import Stack



def     UpperCAmelCase     (    a_		)   ->       int:



   """simple docstring"""






   return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(a_      ,		-1		)



def     UpperCAmelCase     (    a_		)   ->       str:



   """simple docstring"""






   if not balanced_parentheses(a_		):
      raise ValueError("""Mismatched parentheses"""		)
   A_  :		Stack[str]						=					Stack()
   A_  :		Optional[int]						=					[]
   for char in expression_str:
      if char.isalpha() or char.isdigit():
         postfix.append(a_		)
      elif char == "(":
         stack.push(a_		)
      elif char == ")":
         while not stack.is_empty() and stack.peek() != "(":
            postfix.append(stack.pop()		)
         stack.pop()
      else:
         while not stack.is_empty() and precedence(a_		) <= precedence(stack.peek()		):
            postfix.append(stack.pop()		)
         stack.push(a_		)
   while not stack.is_empty():
      postfix.append(stack.pop()		)
   return " ".join(a_		)


if __name__ == "__main__":
  from doctest import testmod

  testmod()
  UpperCamelCase__    :     Dict			  =   'a+b*(c^d-e)^(f+g*h)-i'

  print('Infix to Postfix Notation demonstration:\n')
  print('Infix notation: ' + expression)
  print('Postfix notation: ' + infix_to_postfix(expression))





'''simple docstring'''





from typing import TYPE_CHECKING

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


UpperCamelCase__    :     List[Any]			  =   {
    'configuration_layoutlm': ['LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMConfig', 'LayoutLMOnnxConfig'],
    'tokenization_layoutlm': ['LayoutLMTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    :     Any			  =   ['LayoutLMTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    :     Optional[Any]			  =   [
      'LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'LayoutLMForMaskedLM',
      'LayoutLMForSequenceClassification',
      'LayoutLMForTokenClassification',
      'LayoutLMForQuestionAnswering',
      'LayoutLMModel',
      'LayoutLMPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    :     str			  =   [
      'TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFLayoutLMForMaskedLM',
      'TFLayoutLMForSequenceClassification',
      'TFLayoutLMForTokenClassification',
      'TFLayoutLMForQuestionAnswering',
      'TFLayoutLMMainLayer',
      'TFLayoutLMModel',
      'TFLayoutLMPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
  from .tokenization_layoutlm import LayoutLMTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_layoutlm import (
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        LayoutLMForMaskedLM,
        LayoutLMForQuestionAnswering,
        LayoutLMForSequenceClassification,
        LayoutLMForTokenClassification,
        LayoutLMModel,
        LayoutLMPreTrainedModel,
    )
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_layoutlm import (
        TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMForMaskedLM,
        TFLayoutLMForQuestionAnswering,
        TFLayoutLMForSequenceClassification,
        TFLayoutLMForTokenClassification,
        TFLayoutLMMainLayer,
        TFLayoutLMModel,
        TFLayoutLMPreTrainedModel,
    )

else:
  import sys

  UpperCamelCase__    :     List[str]			  =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
