from typing import TYPE_CHECKING

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


UpperCAmelCase_    		=				{
    '''configuration_segformer''': ['''SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SegformerConfig''', '''SegformerOnnxConfig''']
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_    		=				['''SegformerFeatureExtractor''']
      UpperCAmelCase_    		=				['''SegformerImageProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_    		=				[
          '''SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''SegformerDecodeHead''',
          '''SegformerForImageClassification''',
          '''SegformerForSemanticSegmentation''',
          '''SegformerLayer''',
          '''SegformerModel''',
          '''SegformerPreTrainedModel''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_    		=				[
          '''TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFSegformerDecodeHead''',
          '''TFSegformerForImageClassification''',
          '''TFSegformerForSemanticSegmentation''',
          '''TFSegformerModel''',
          '''TFSegformerPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_segformer import SegformerFeatureExtractor
            from .image_processing_segformer import SegformerImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_segformer import (
                SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                SegformerDecodeHead,
                SegformerForImageClassification,
                SegformerForSemanticSegmentation,
                SegformerLayer,
                SegformerModel,
                SegformerPreTrainedModel,
            )
      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_segformer import (
                TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFSegformerDecodeHead,
                TFSegformerForImageClassification,
                TFSegformerForSemanticSegmentation,
                TFSegformerModel,
                TFSegformerPreTrainedModel,
            )

else:
      import sys

      UpperCAmelCase_    		=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



from .balanced_parentheses import balanced_parentheses
from .stack import Stack

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str	)     ->	int:



	"""simple docstring"""






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

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str	)     ->	str:



	"""simple docstring"""






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


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

			testmod()
			_A							     =       '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))