



"""simple docstring"""




UpperCAmelCase								=   {
    """+""": lambda x, y: x + y,
    """-""": lambda x, y: x - y,
    """*""": lambda x, y: x * y,
    """/""": lambda x, y: x / y,
}

def      A_ (		__a       :			str						):

				"""simple docstring"""
				return c.isdigit()

def      A_ (		__a       :			Dict						):

				"""simple docstring"""
				a__			 =						[]

				# iterate over the string in reverse order
				for c in expression.split()[::-1]:
								# push operand to stack
								if is_operand(__a						):
												stack.append(int(__a						)						)

								else:
												# pop values from stack can calculate the result
												# push the result onto the stack again
												a__			 =						stack.pop()
												a__			 =						stack.pop()
												stack.append(calc[c](__a	,						__a						)						)

				return stack.pop()


# Driver code
if __name__ == "__main__":
	UpperCAmelCase								=   """+ 9 * 2 6"""
	print(evaluate(test_expression))

	UpperCAmelCase								=   """/ * 10 2 + 4 1 """
	print(evaluate(test_expression))






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase								=   logging.get_logger(__name__)

UpperCAmelCase								=   {
    """facebook/regnet-y-040""": """https://huggingface.co/facebook/regnet-y-040/blob/main/config.json""",
}

class 	__snake_case (				SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__			: Tuple             =   """regnet"""
							UpperCamelCase__			: int             =   ["""x""", """y"""]


							def __init__(							self					,					a_=3					,					a_=32					,					a_=[128, 192, 512, 1_088]					,					a_=[2, 6, 12, 2]					,					a_=64					,					a_="y"					,					a_="relu"					,					**a_					,					):
											super().__init__(**a_						)
											if layer_type not in self.layer_types:
															raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types						)}'''						)
											a__			 =						num_channels
											a__			 =						embedding_size
											a__			 =						hidden_sizes
											a__			 =						depths
											a__			 =						groups_width
											a__			 =						layer_type
											a__			 =						hidden_act
											# always downsample in the first stage
											a__			 =						True

