





UpperCamelCase__ :			Optional[Any]       	=			{
    """+""": lambda x, y: x + y,
    """-""": lambda x, y: x - y,
    """*""": lambda x, y: x * y,
    """/""": lambda x, y: x / y,
}




def  A_(      A ):
	return c.isdigit()




def  A_(      A ):
	UpperCAmelCase_		    =  []

	# 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
			UpperCAmelCase_		    =  stack.pop()
			UpperCAmelCase_		    =  stack.pop()
			stack.append(calc[c](A					,   A ) )

	return stack.pop()


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

				UpperCamelCase__ :			Optional[int]       	=			"""/ * 10 2 + 4 1 """
				print(evaluate(test_expression))









from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
				import pyximport

				pyximport.install(setup_args={"""include_dirs""": np.get_include()})
				from . import algos_graphormer  # noqa E402




def  A_(      A					,   A = 512 ):
	UpperCAmelCase_		    =  x.shape[1] if len(x.shape ) > 1 else 1
	UpperCAmelCase_		    =  1 + np.arange(0					,   feature_num * offset					,   A					,   dtype=np.intaa )
	UpperCAmelCase_		    =  x + feature_offset
	return x




def  A_(      A					,   A=True ):
	requires_backends(A					,   ["""cython"""] )

	if keep_features and "edge_attr" in item.keys():  # edge_attr
		UpperCAmelCase_		    =  np.asarray(item["""edge_attr"""]					,   dtype=np.intaa )
	else:
		UpperCAmelCase_		    =  np.ones((len(item["""edge_index"""][0] ), 1)					,   dtype=np.intaa )  # same embedding for all

	if keep_features and "node_feat" in item.keys():  # input_nodes
		UpperCAmelCase_		    =  np.asarray(item["""node_feat"""]					,   dtype=np.intaa )
	else:
		UpperCAmelCase_		    =  np.ones((item["""num_nodes"""], 1)					,   dtype=np.intaa )  # same embedding for all

	UpperCAmelCase_		    =  np.asarray(item["""edge_index"""]					,   dtype=np.intaa )

	UpperCAmelCase_		    =  convert_to_single_emb(A ) + 1
	UpperCAmelCase_		    =  item["""num_nodes"""]

	if len(edge_attr.shape ) == 1:
		UpperCAmelCase_		    =  edge_attr[:, None]
	UpperCAmelCase_		    =  np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]					,   dtype=np.intaa )
	UpperCAmelCase_		    =  convert_to_single_emb(A ) + 1

	# node adj matrix [num_nodes, num_nodes] bool
	UpperCAmelCase_		    =  np.zeros([num_nodes, num_nodes]					,   dtype=A )
	UpperCAmelCase_		    =  True

	UpperCAmelCase_    ,						UpperCAmelCase_		    =  algos_graphormer.floyd_warshall(A )
	UpperCAmelCase_		    =  np.amax(A )

	UpperCAmelCase_		    =  algos_graphormer.gen_edge_input(A					,   A					,   A )
	UpperCAmelCase_		    =  np.zeros([num_nodes + 1, num_nodes + 1]					,   dtype=np.single )  # with graph token

	# combine
	UpperCAmelCase_		    =  input_nodes + 1  # we shift all indices by one for padding
	UpperCAmelCase_		    =  attn_bias
	UpperCAmelCase_		    =  attn_edge_type
	UpperCAmelCase_		    =  shortest_path_result.astype(np.intaa ) + 1  # we shift all indices by one for padding
	UpperCAmelCase_		    =  np.sum(A					,   axis=1 ).reshape(-1 ) + 1  # we shift all indices by one for padding
	UpperCAmelCase_		    =  item["""in_degree"""]  # for undirected graph
	UpperCAmelCase_		    =  input_edges + 1  # we shift all indices by one for padding
	if "labels" not in item:
		UpperCAmelCase_		    =  item["""y"""]

	return item







class 	_UpperCamelCase	:







					'''simple docstring'''

					def __init__(    self     :	str			,       __lowercase     :	int=20			,       __lowercase     :	Union[str, Any]=False		):






						'''simple docstring'''







						if not is_cython_available():
							raise ImportError("""Graphormer preprocessing needs Cython (pyximport)"""		)

						UpperCAmelCase_		    =  spatial_pos_max
						UpperCAmelCase_		    =  on_the_fly_processing





					def __call__(    self     :	List[str]			,       __lowercase     :	List[dict]		):






						'''simple docstring'''







						if self.on_the_fly_processing:
							UpperCAmelCase_		    =  [preprocess_item(__lowercase		) for i in features]

						if not isinstance(features[0]			,       __lowercase		):
							UpperCAmelCase_		    =  [vars(__lowercase		) for f in features]
						UpperCAmelCase_		    =  {}

						UpperCAmelCase_		    =  max(len(i["""input_nodes"""]		) for i in features		)
						UpperCAmelCase_		    =  len(features[0]["""input_nodes"""][0]		)
						UpperCAmelCase_		    =  len(features[0]["""attn_edge_type"""][0][0]		)
						UpperCAmelCase_		    =  max(len(i["""input_edges"""][0][0]		) for i in features		)
						UpperCAmelCase_		    =  len(features[0]["""input_edges"""][0][0][0]		)
						UpperCAmelCase_		    =  len(__lowercase		)

						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       max_node_num + 1			,       max_node_num + 1			,       dtype=torch.float		)
						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       dtype=torch.long		)
						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       __lowercase			,       __lowercase			,       dtype=torch.long		)
						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       __lowercase			,       dtype=torch.long		)
						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       __lowercase			,       __lowercase			,       dtype=torch.long		)
						UpperCAmelCase_		    =  torch.zeros(
						    __lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       dtype=torch.long		)

						for ix, f in enumerate(__lowercase		):
							for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
								UpperCAmelCase_		    =  torch.tensor(f[k]		)

							if len(f["""attn_bias"""][1:, 1:][f["""spatial_pos"""] >= self.spatial_pos_max]		) > 0:
								UpperCAmelCase_		    =  float("""-inf"""		)

							UpperCAmelCase_		    =  f["""attn_bias"""]
							UpperCAmelCase_		    =  f[
							    """attn_edge_type"""
							]
							UpperCAmelCase_		    =  f["""spatial_pos"""]
							UpperCAmelCase_		    =  f["""in_degree"""]
							UpperCAmelCase_		    =  f["""input_nodes"""]
							UpperCAmelCase_		    =  f["""input_edges"""]

						UpperCAmelCase_		    =  batch["""in_degree"""]

						UpperCAmelCase_		    =  features[0]["""labels"""]
						if len(__lowercase		) == 1:  # one task
							if isinstance(sample[0]			,       __lowercase		):  # regression
								UpperCAmelCase_		    =  torch.from_numpy(np.concatenate([i["""labels"""] for i in features]		)		)
							else:  # binary classification
								UpperCAmelCase_		    =  torch.from_numpy(np.concatenate([i["""labels"""] for i in features]		)		)
						else:  # multi task classification, left to float to keep the NaNs
							UpperCAmelCase_		    =  torch.from_numpy(np.stack([i["""labels"""] for i in features]			,       axis=0		)		)

						return batch


