



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 	lowercase_							(A		, A = 5_1_2				):
       snake_case__							:     Optional[int]            =     x.shape[1] if len(x.shape				) > 1 else 1
       snake_case__							:     List[str]            =     1 + np.arange(0		, feature_num * offset		, lowercase__		, dtype=np.intaa				)
       snake_case__							:     Tuple            =     x + feature_offset
       return x
def 	lowercase_							(A		, A=True				):
       requires_backends(lowercase__		, ['cython']				)

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

       snake_case__							:     int            =     np.asarray(item['edge_index']		, dtype=np.intaa				)

       snake_case__							:     List[Any]            =     convert_to_single_emb(lowercase__				) + 1
       snake_case__							:     Any            =     item["num_nodes"]

       if len(edge_attr.shape				) == 1:
              snake_case__							:     int            =     edge_attr[:, None]
       snake_case__							:     int            =     np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]		, dtype=np.intaa				)
       snake_case__							:     Optional[Any]            =     convert_to_single_emb(lowercase__				) + 1

       # node adj matrix [num_nodes, num_nodes] bool
       snake_case__							:     Union[str, Any]            =     np.zeros([num_nodes, num_nodes]		, dtype=lowercase__				)
       snake_case__							:     Union[str, Any]            =     True

       snake_case__							:     List[str]            =     algos_graphormer.floyd_warshall(lowercase__				)
       snake_case__							:     Union[str, Any]            =     np.amax(lowercase__				)

       snake_case__							:     Dict            =     algos_graphormer.gen_edge_input(lowercase__		, lowercase__		, lowercase__				)
       snake_case__							:     int            =     np.zeros([num_nodes + 1, num_nodes + 1]		, dtype=np.single				)  # with graph token

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

       return item





class  snake_case__						:






     """simple docstring"""


     def __init__(	self  :  Any,			_snake_case  :  Union[str, Any]=2_0,			_snake_case  :  Dict=False )       ->Dict:







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

            snake_case__							:     Optional[Any]            =     spatial_pos_max
            snake_case__							:     List[Any]            =     on_the_fly_processing






     def __call__(	self  :  Dict,			_snake_case  :  Any )       ->Union[str, Any]:







            if self.on_the_fly_processing:
                   snake_case__							:     str            =     [preprocess_item(_snake_case ) for i in features]

            if not isinstance(features[0],			_snake_case ):
                   snake_case__							:     Dict            =     [vars(_snake_case ) for f in features]
            snake_case__							:     int            =     {}

            snake_case__							:     Union[str, Any]            =     max(len(i['input_nodes'] ) for i in features )
            snake_case__							:     Tuple            =     len(features[0]['input_nodes'][0] )
            snake_case__							:     Optional[Any]            =     len(features[0]['attn_edge_type'][0][0] )
            snake_case__							:     Tuple            =     max(len(i['input_edges'][0][0] ) for i in features )
            snake_case__							:     Optional[int]            =     len(features[0]['input_edges'][0][0][0] )
            snake_case__							:     List[str]            =     len(_snake_case )

            snake_case__							:     Tuple            =     torch.zeros(_snake_case,			max_node_num + 1,			max_node_num + 1,			dtype=torch.float )
            snake_case__							:     Dict            =     torch.zeros(_snake_case,			_snake_case,			_snake_case,			_snake_case,			dtype=torch.long )
            snake_case__							:     Optional[int]            =     torch.zeros(_snake_case,			_snake_case,			_snake_case,			dtype=torch.long )
            snake_case__							:     Dict            =     torch.zeros(_snake_case,			_snake_case,			dtype=torch.long )
            snake_case__							:     str            =     torch.zeros(_snake_case,			_snake_case,			_snake_case,			dtype=torch.long )
            snake_case__							:     Tuple            =     torch.zeros(
                _snake_case,			_snake_case,			_snake_case,			_snake_case,			_snake_case,			dtype=torch.long )

            for ix, f in enumerate(_snake_case ):
                   for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
                          snake_case__							:     Union[str, Any]            =     torch.tensor(f[k] )

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

                   snake_case__							:     Any            =     f["attn_bias"]
                   snake_case__							:     List[Any]            =     f[
                       "attn_edge_type"
                   ]
                   snake_case__							:     Tuple            =     f["spatial_pos"]
                   snake_case__							:     Optional[int]            =     f["in_degree"]
                   snake_case__							:     int            =     f["input_nodes"]
                   snake_case__							:     List[Any]            =     f["input_edges"]

            snake_case__							:     Tuple            =     batch["in_degree"]

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

            return batch











def 	lowercase_							(A			:			list				):
       for i, _ in enumerate(A				):
              if (i % 2 == 1) == (nums[i - 1] > nums[i]):
                     snake_case__     ,    snake_case__							:     List[str]            =     nums[i], nums[i - 1]

       return nums


if __name__ == "__main__":
 print("Enter the array elements:")
 a_       :Tuple				  =				list(map(int, input().split()))
 print("The unsorted array is:")
 print(array)
 print("Array after Wiggle sort:")
 print(wiggle_sort(array))






