





"""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 = 512 ):






  """simple docstring"""





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





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase=True ):






  """simple docstring"""





  requires_backends(_UpperCAmelCase			, ['cython'] )

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

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

  A_  :	Optional[int]    						=  convert_to_single_emb(_UpperCAmelCase ) + 1
  A_  :	str    						=  item['num_nodes']

  if len(edge_attr.shape ) == 1:
    A_  :	List[Any]    						=  edge_attr[:, None]
  A_  :	Union[str, Any]    						=  np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]			, dtype=np.intaa )
  A_  :	Optional[Any]    						=  convert_to_single_emb(_UpperCAmelCase ) + 1

  # node adj matrix [num_nodes, num_nodes] bool
  A_  :	List[str]    						=  np.zeros([num_nodes, num_nodes]			, dtype=_UpperCAmelCase )
  A_  :	List[str]    						=  True

  A_						,	A_  :	int    						=  algos_graphormer.floyd_warshall(_UpperCAmelCase )
  A_  :	Optional[Any]    						=  np.amax(_UpperCAmelCase )

  A_  :	Optional[Any]    						=  algos_graphormer.gen_edge_input(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase )
  A_  :	int    						=  np.zeros([num_nodes + 1, num_nodes + 1]			, dtype=np.single )  # with graph token

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

  return item





class 			_UpperCAmelCase  :



      '''simple docstring'''

      def __init__(					self ,				snake_case_=2_0 ,				snake_case_=False  ):

        """simple docstring"""

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

        A_  :	Tuple    						=  spatial_pos_max
        A_  :	List[Any]    						=  on_the_fly_processing

      def __call__(					self ,				snake_case_  ):

        """simple docstring"""

        if self.on_the_fly_processing:
          A_  :	Optional[int]    						=  [preprocess_item(snake_case_  ) for i in features]

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

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

        A_  :	List[Any]    						=  torch.zeros(snake_case_ ,				max_node_num + 1 ,				max_node_num + 1 ,				dtype=torch.float  )
        A_  :	str    						=  torch.zeros(snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				dtype=torch.long  )
        A_  :	Dict    						=  torch.zeros(snake_case_ ,				snake_case_ ,				snake_case_ ,				dtype=torch.long  )
        A_  :	Optional[int]    						=  torch.zeros(snake_case_ ,				snake_case_ ,				dtype=torch.long  )
        A_  :	Tuple    						=  torch.zeros(snake_case_ ,				snake_case_ ,				snake_case_ ,				dtype=torch.long  )
        A_  :	Any    						=  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"]:
            A_  :	str    						=  torch.tensor(f[k]  )

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

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

        A_  :	Optional[Any]    						=  batch['in_degree']

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

        return batch






"""simple docstring"""




def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Any    						=  len(_UpperCAmelCase )
  # Create a stack and push index of fist element to it
  A_  :	List[Any]    						=  []
  st.append(0 )

  # Span value of first element is always 1
  A_  :	List[str]    						=  1

  # Calculate span values for rest of the elements
  for i in range(1			, _UpperCAmelCase ):
    # Pop elements from stack while stack is not
    # empty and top of stack is smaller than price[i]
    while len(_UpperCAmelCase ) > 0 and price[st[0]] <= price[i]:
      st.pop()

    # If stack becomes empty, then price[i] is greater
    # than all elements on left of it, i.e. price[0],
    # price[1], ..price[i-1]. Else the price[i]  is
    # greater than elements after top of stack
    A_  :	Union[str, Any]    						=  i + 1 if len(_UpperCAmelCase ) <= 0 else (i - st[0])

    # Push this element to stack
    st.append(_UpperCAmelCase )





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  for i in range(0			, _UpperCAmelCase ):
    print(arr[i]			, end=' ' )


# Driver program to test above function
lowerCamelCase_					:						List[Any]      = [10, 4, 5, 90, 1_20, 80]
lowerCamelCase_					:						Optional[Any]      = [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)





print_array(S, len(price))