



'''simple docstring'''







from .stack import Stack







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Stack[str]										=      Stack()
		SCREAMING_SNAKE_CASE_						:List[str]										=      {'(': ')', '[': ']', '{': '}'}
		for bracket in parentheses:
				if bracket in bracket_pairs:
						stack.push(SCREAMING_SNAKE_CASE     )
				elif bracket in (")", "]", "}"):
						if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
								return False
		return stack.is_empty()


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

							testmod()

							SCREAMING_SNAKE_CASE__     : Optional[int]				=   ["""((()))""", """((())""", """(()))"""]
							print("""Balanced parentheses demonstration:\n""")
							for example in examples:
														SCREAMING_SNAKE_CASE__     : str				=   """""" if balanced_parentheses(example) else """not """
														print(f"""{example} is {not_str}balanced""")








'''simple docstring'''







from typing import TYPE_CHECKING

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


_A      :     Tuple       	={
    '''configuration_imagegpt''': ['''IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ImageGPTConfig''', '''ImageGPTOnnxConfig''']
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     Dict       	=['''ImageGPTFeatureExtractor''']
			_A      :     Union[str, Any]       	=['''ImageGPTImageProcessor''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     str       	=[
			    '''IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''ImageGPTForCausalImageModeling''',
			    '''ImageGPTForImageClassification''',
			    '''ImageGPTModel''',
			    '''ImageGPTPreTrainedModel''',
			    '''load_tf_weights_in_imagegpt''',
			]


if TYPE_CHECKING:
			from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
						from .image_processing_imagegpt import ImageGPTImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_imagegpt import (
						    IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ImageGPTForCausalImageModeling,
						    ImageGPTForImageClassification,
						    ImageGPTModel,
						    ImageGPTPreTrainedModel,
						    load_tf_weights_in_imagegpt,
						)

else:
			import sys

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

