


from typing import TYPE_CHECKING

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


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

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

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

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A							     =       [
			    '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

			_A							     =       _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))