





"""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 		lowerCAmelCase_     (  lowercase_       :							int			,				lowercase_       :							int = 512		) ->    int:

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	x.shape[1] if len(x.shape		) > 1 else 1
 __SCREAMING_SNAKE_CASE			:      List[str] 							=	1 + np.arange(0			,				feature_num * offset			,				lowercase__			,				dtype=np.intaa		)
 __SCREAMING_SNAKE_CASE			:      Tuple 							=	x + feature_offset
 return x




def 		lowerCAmelCase_     (  lowercase_       :							int			,				lowercase_       :							List[Any]=True		) ->    Dict:

 '''simple docstring'''






 requires_backends(lowercase__			,				['''cython''']		)

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

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

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

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

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

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

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

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

 return item






class       snake_case		:

      def __init__(      self       :Tuple						,							_lowerCamelCase       :Union[str, Any]=2_0						,							_lowerCamelCase       :Optional[Any]=False						):







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

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



      def __call__(      self       :int						,							_lowerCamelCase       :Union[str, Any]						):







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

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

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

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

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

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

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

       __SCREAMING_SNAKE_CASE			:      Tuple 							=	batch["in_degree"]

       __SCREAMING_SNAKE_CASE			:      str 							=	features[0]["labels"]
       if len(_lowerCamelCase						) == 1:  # one task
        if isinstance(sample[0]						,							_lowerCamelCase						):  # regression
         __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	torch.from_numpy(np.concatenate([i['''labels'''] for i in features]						)						)
        else:  # binary classification
         __SCREAMING_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
        __SCREAMING_SNAKE_CASE			:      List[str] 							=	torch.from_numpy(np.stack([i['''labels'''] for i in features]						,							axis=0						)						)

       return batch








"""simple docstring"""





import math





class       snake_case		:

      def __init__(      self       :Optional[Any]						,							_lowerCamelCase       :Dict						):
       __SCREAMING_SNAKE_CASE			:      str 							=	len(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	[0] * (
           4 * self.N
       )  # approximate the overall size of segment tree with array N
       self.build(1						,							0						,							self.N - 1						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :Union[str, Any]						,							_lowerCamelCase       :List[str]						):
       return idx * 2

      def 						SCREAMING_SNAKE_CASE_      (      self       :Tuple						,							_lowerCamelCase       :Optional[Any]						):
       return idx * 2 + 1

      def 						SCREAMING_SNAKE_CASE_      (      self       :int						,							_lowerCamelCase       :Optional[Any]						,							_lowerCamelCase       :Union[str, Any]						,							_lowerCamelCase       :str						):  # noqa: E741
       if l == r:
        __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	A[l]
       else:
        __SCREAMING_SNAKE_CASE			:      Dict 							=	(l + r) // 2
        self.build(self.left(_lowerCamelCase						)						,							_lowerCamelCase						,							_lowerCamelCase						)
        self.build(self.right(_lowerCamelCase						)						,							mid + 1						,							_lowerCamelCase						)
        __SCREAMING_SNAKE_CASE			:      List[Any] 							=	max(self.st[self.left(_lowerCamelCase						)]						,							self.st[self.right(_lowerCamelCase						)]						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :Tuple						,							_lowerCamelCase       :Optional[Any]						,							_lowerCamelCase       :Optional[Any]						,							_lowerCamelCase       :Union[str, Any]						):
       return self.update_recursive(1						,							0						,							self.N - 1						,							a - 1						,							b - 1						,							_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[int]						,							_lowerCamelCase       :Optional[Any]						,							_lowerCamelCase       :Any						,							_lowerCamelCase       :Union[str, Any]						,							_lowerCamelCase       :Optional[Any]						,							_lowerCamelCase       :str						,							_lowerCamelCase       :Optional[Any]						):  # noqa: E741
       if r < a or l > b:
        return True
       if l == r:
        __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	val
        return True
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	(l + r) // 2
       self.update_recursive(self.left(_lowerCamelCase						)						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)
       self.update_recursive(self.right(_lowerCamelCase						)						,							mid + 1						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	max(self.st[self.left(_lowerCamelCase						)]						,							self.st[self.right(_lowerCamelCase						)]						)
       return True

      def 						SCREAMING_SNAKE_CASE_      (      self       :Tuple						,							_lowerCamelCase       :str						,							_lowerCamelCase       :Any						):
       return self.query_recursive(1						,							0						,							self.N - 1						,							a - 1						,							b - 1						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :str						,							_lowerCamelCase       :Dict						,							_lowerCamelCase       :str						,							_lowerCamelCase       :Union[str, Any]						,							_lowerCamelCase       :Optional[Any]						,							_lowerCamelCase       :Any						):  # noqa: E741
       if r < a or l > b:
        return -math.inf
       if l >= a and r <= b:
        return self.st[idx]
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	(l + r) // 2
       __SCREAMING_SNAKE_CASE			:      Any 							=	self.query_recursive(self.left(_lowerCamelCase						)						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	self.query_recursive(self.right(_lowerCamelCase						)						,							mid + 1						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)
       return max(_lowerCamelCase						,							_lowerCamelCase						)



      def 						SCREAMING_SNAKE_CASE_      (      self       :Dict						):
       __SCREAMING_SNAKE_CASE			:      str 							=	[]
       for i in range(1						,							N + 1						):
        show_list += [self.query(_lowerCamelCase						,							_lowerCamelCase						)]
       print(_lowerCamelCase						)


if __name__ == "__main__":
    _lowerCamelCase  					=     [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
    _lowerCamelCase  					=     15
    _lowerCamelCase  					=     SegmentTree(A)
    print(segt.query(4, 6))
    print(segt.query(7, 11))
    print(segt.query(7, 12))
    segt.update(1, 3, 1_11)
    print(segt.query(1, 15))
    segt.update(7, 8, 2_35)
    segt.show_data()

