




"""simple docstring"""




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       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase = 5_12					):
			__lowercase	:       str				=   x.shape[1] if len(x.shape					) > 1 else 1
			__lowercase	:       Union[str, Any]				=   1 + np.arange(0					,							feature_num * offset					,							__UpperCamelCase					,							dtype=np.intaa					)
			__lowercase	:       Tuple				=   x + feature_offset
			return x







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase=True					):
			requires_backends(__UpperCamelCase					,							['''cython''']					)

			if keep_features and "edge_attr" in item.keys():  # edge_attr
						__lowercase	:       Any				=   np.asarray(item['''edge_attr''']					,							dtype=np.intaa					)
			else:
						__lowercase	:       Dict				=   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
						__lowercase	:       Tuple				=   np.asarray(item['''node_feat''']					,							dtype=np.intaa					)
			else:
						__lowercase	:       Dict				=   np.ones((item['''num_nodes'''], 1)					,							dtype=np.intaa					)  # same embedding for all

			__lowercase	:       List[Any]				=   np.asarray(item['''edge_index''']					,							dtype=np.intaa					)

			__lowercase	:       str				=   convert_to_single_emb(__UpperCamelCase					) + 1
			__lowercase	:       str				=   item['''num_nodes''']

			if len(edge_attr.shape					) == 1:
						__lowercase	:       int				=   edge_attr[:, None]
			__lowercase	:       Dict				=   np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]					,							dtype=np.intaa					)
			__lowercase	:       List[str]				=   convert_to_single_emb(__UpperCamelCase					) + 1

			# node adj matrix [num_nodes, num_nodes] bool
			__lowercase	:       List[Any]				=   np.zeros([num_nodes, num_nodes]					,							dtype=__UpperCamelCase					)
			__lowercase	:       Optional[Any]				=   True

			__lowercase	,__lowercase	:       Any				=   algos_graphormer.floyd_warshall(__UpperCamelCase					)
			__lowercase	:       Optional[Any]				=   np.amax(__UpperCamelCase					)

			__lowercase	:       List[str]				=   algos_graphormer.gen_edge_input(__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					)
			__lowercase	:       Dict				=   np.zeros([num_nodes + 1, num_nodes + 1]					,							dtype=np.single					)  # with graph token

			# combine
			__lowercase	:       Optional[Any]				=   input_nodes + 1  # we shift all indices by one for padding
			__lowercase	:       str				=   attn_bias
			__lowercase	:       List[str]				=   attn_edge_type
			__lowercase	:       Union[str, Any]				=   shortest_path_result.astype(np.intaa					) + 1  # we shift all indices by one for padding
			__lowercase	:       Optional[int]				=   np.sum(__UpperCamelCase					,							axis=1					).reshape(-1					) + 1  # we shift all indices by one for padding
			__lowercase	:       Dict				=   item['''in_degree''']  # for undirected graph
			__lowercase	:       int				=   input_edges + 1  # we shift all indices by one for padding
			if "labels" not in item:
						__lowercase	:       List[str]				=   item['''y''']

			return item


class        UpperCAmelCase_  :
		def __init__(		self							,		UpperCamelCase_=20							,		UpperCamelCase_=False   )		->   Dict:
					if not is_cython_available():
								raise ImportError('''Graphormer preprocessing needs Cython (pyximport)'''   )

					__lowercase	:       List[str]				=   spatial_pos_max
					__lowercase	:       str				=   on_the_fly_processing
		def __call__(		self							,		UpperCamelCase_   )		->   Dict[str, Any]:
					if self.on_the_fly_processing:
								__lowercase	:       Optional[int]				=   [preprocess_item(UpperCamelCase_   ) for i in features]

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

					__lowercase	:       str				=   max(len(i['''input_nodes''']   ) for i in features   )
					__lowercase	:       Optional[int]				=   len(features[0]['''input_nodes'''][0]   )
					__lowercase	:       Optional[int]				=   len(features[0]['''attn_edge_type'''][0][0]   )
					__lowercase	:       Union[str, Any]				=   max(len(i['''input_edges'''][0][0]   ) for i in features   )
					__lowercase	:       Optional[int]				=   len(features[0]['''input_edges'''][0][0][0]   )
					__lowercase	:       Union[str, Any]				=   len(UpperCamelCase_   )

					__lowercase	:       Union[str, Any]				=   torch.zeros(UpperCamelCase_							,		max_node_num + 1							,		max_node_num + 1							,		dtype=torch.float   )
					__lowercase	:       Optional[int]				=   torch.zeros(UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		dtype=torch.long   )
					__lowercase	:       List[Any]				=   torch.zeros(UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		dtype=torch.long   )
					__lowercase	:       int				=   torch.zeros(UpperCamelCase_							,		UpperCamelCase_							,		dtype=torch.long   )
					__lowercase	:       List[Any]				=   torch.zeros(UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		dtype=torch.long   )
					__lowercase	:       int				=   torch.zeros(
					    UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		dtype=torch.long   )

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

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

								__lowercase	:       Optional[Any]				=   f['''attn_bias''']
								__lowercase	:       List[str]				=   f[
								    '''attn_edge_type'''
								]
								__lowercase	:       str				=   f['''spatial_pos''']
								__lowercase	:       Optional[Any]				=   f['''in_degree''']
								__lowercase	:       int				=   f['''input_nodes''']
								__lowercase	:       Optional[Any]				=   f['''input_edges''']

					__lowercase	:       Optional[int]				=   batch['''in_degree''']

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

					return batch









"""simple docstring"""




from __future__ import annotations







def       __UpperCAmelCase      (				__UpperCamelCase					):
			__lowercase	:       List[Any]				=   [0] * len(__UpperCamelCase					)
			for i in range(1					,							len(__UpperCamelCase					)					):
						__lowercase	:       Optional[Any]				=   duration_times[i - 1] + waiting_times[i - 1]
			return waiting_times







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					):
			return [
			    duration_time + waiting_times[i]
			    for i, duration_time in enumerate(__UpperCamelCase					)
			]







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return sum(__UpperCamelCase					) / len(__UpperCamelCase					)







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return sum(__UpperCamelCase					) / len(__UpperCamelCase					)


if __name__ == "__main__":
			# process id's
			a_            =   [1, 2, 3]

			# ensure that we actually have processes
			if len(processes) == 0:
						print('Zero amount of processes')
						raise SystemExit(0)

			# duration time of all processes
			a_            =   [1_9, 8, 9]

			# ensure we can match each id to a duration time
			if len(duration_times) != len(processes):
						print('Unable to match all id\'s with their duration time')
						raise SystemExit(0)

			# get the waiting times and the turnaround times
			a_            =   calculate_waiting_times(duration_times)
			a_            =   calculate_turnaround_times(duration_times, waiting_times)

			# get the average times
			a_            =   calculate_average_waiting_time(waiting_times)
			a_            =   calculate_average_turnaround_time(turnaround_times)

			# print all the results
			print('Process ID\tDuration Time\tWaiting Time\tTurnaround Time')
			for i, process in enumerate(processes):
						print(
						    F"{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"
						    F"{turnaround_times[i]}"
						)
			print(F"Average waiting time = {average_waiting_time}")
			print(F"Average turn around time = {average_turnaround_time}")



