




'''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 					a__    (				UpperCamelCase_	:			Optional[Any],      UpperCamelCase_	:			Tuple = 512	):
   UpperCAmelCase__  :Any            =			x.shape[1] if len(x.shape	) > 1 else 1
   UpperCAmelCase__  :Union[str, Any]            =			1 + np.arange(0,      feature_num * offset,      UpperCamelCase_,      dtype=np.intaa	)
   UpperCAmelCase__  :Optional[Any]            =			x + feature_offset
   return x


def 					a__    (				UpperCamelCase_	:			Any,      UpperCamelCase_	:			Any=True	):
   requires_backends(UpperCamelCase_,      ['''cython''']	)

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

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

   UpperCAmelCase__  :Any            =			convert_to_single_emb(UpperCamelCase_	) + 1
   UpperCAmelCase__  :Dict            =			item["""num_nodes"""]

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

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

   UpperCAmelCase__  :Any            =			algos_graphormer.floyd_warshall(UpperCamelCase_	)
   UpperCAmelCase__  :Tuple            =			np.amax(UpperCamelCase_	)

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

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

   return item




class 							UpperCAmelCase :


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

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

    UpperCAmelCase__  :str            =			spatial_pos_max
    UpperCAmelCase__  :Dict            =			on_the_fly_processing





 def __call__(			self       :  List[str] ,					__lowerCamelCase       :  Optional[Any]   ):

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

    if not isinstance(features[0] ,					_lowerCamelCase   ):
       UpperCAmelCase__  :Tuple            =			[vars(_lowerCamelCase   ) for f in features]
    UpperCAmelCase__  :Any            =			{}

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

    UpperCAmelCase__  :Dict            =			torch.zeros(_lowerCamelCase ,					max_node_num + 1 ,					max_node_num + 1 ,					dtype=torch.float   )
    UpperCAmelCase__  :str            =			torch.zeros(_lowerCamelCase ,					_lowerCamelCase ,					_lowerCamelCase ,					_lowerCamelCase ,					dtype=torch.long   )
    UpperCAmelCase__  :Optional[Any]            =			torch.zeros(_lowerCamelCase ,					_lowerCamelCase ,					_lowerCamelCase ,					dtype=torch.long   )
    UpperCAmelCase__  :Optional[Any]            =			torch.zeros(_lowerCamelCase ,					_lowerCamelCase ,					dtype=torch.long   )
    UpperCAmelCase__  :Union[str, Any]            =			torch.zeros(_lowerCamelCase ,					_lowerCamelCase ,					_lowerCamelCase ,					dtype=torch.long   )
    UpperCAmelCase__  :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"]:
          UpperCAmelCase__  :int            =			torch.tensor(f[k]   )

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

       UpperCAmelCase__  :Tuple            =			f["""attn_bias"""]
       UpperCAmelCase__  :Dict            =			f[
           """attn_edge_type"""
       ]
       UpperCAmelCase__  :Optional[int]            =			f["""spatial_pos"""]
       UpperCAmelCase__  :List[str]            =			f["""in_degree"""]
       UpperCAmelCase__  :Tuple            =			f["""input_nodes"""]
       UpperCAmelCase__  :int            =			f["""input_edges"""]

    UpperCAmelCase__  :Dict            =			batch["""in_degree"""]

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

    return batch







'''simple docstring'''





def 					a__    (				UpperCamelCase_	:			int	):
   UpperCAmelCase__  :str            =			[True for i in range(n + 1	)]
   UpperCAmelCase__  :List[str]            =			[i - 1 for i in range(n + 1	)]
   UpperCAmelCase__  :str            =			[]
   for i in range(2,      n + 1	):
      if is_prime[i]:
         primes.append(UpperCamelCase_	)
      for j in range(0,      len(UpperCamelCase_	)	):
         if i * primes[j] >= n:
            break
         UpperCAmelCase__  :List[str]            =			False

         if i % primes[j] == 0:
            UpperCAmelCase__  :Any            =			totients[i] * primes[j]
            break

         UpperCAmelCase__  :str            =			totients[i] * (primes[j] - 1)

   return totients


if __name__ == "__main__":
    import doctest

    doctest.testmod()



