

"""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__	(	lowerCAmelCase ,  lowerCAmelCase = 5_12						)  ->   int:
      UpperCAmelCase__ :       Any						=						x.shape[1] if len(x.shape						) > 1 else 1
      UpperCAmelCase__ :       Union[str, Any]						=						1 + np.arange(0 ,  feature_num * offset ,  lowerCAmelCase ,  dtype=np.intaa						)
      UpperCAmelCase__ :       Optional[Any]						=						x + feature_offset
      return x




def 				a__	(	lowerCAmelCase ,  lowerCAmelCase=True						)  ->   Optional[Any]:
      requires_backends(lowerCAmelCase ,  ["""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(lowerCAmelCase						) + 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(lowerCAmelCase						) + 1

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

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

      UpperCAmelCase__ :       Dict						=						algos_graphormer.gen_edge_input(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)
      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(lowerCAmelCase ,  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 			lowerCamelCase  :


  '''simple docstring'''







  def __init__(self       ,					_lowerCamelCase=20       ,					_lowerCamelCase=False  ):


        """simple docstring"""

        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       ,					_lowerCamelCase  ):


        """simple docstring"""

        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"""
import requests

try:
    from django.contrib.auth import authenticate, login
    from django.shortcuts import redirect, render
except ImportError:
    _A						=     _A						=     _A						=     _A						=     print




def 				a__	(	lowerCAmelCase						)  ->   str:
      # Enter your recaptcha secret key here
      UpperCAmelCase__ :       int						=						"""secretKey"""
      UpperCAmelCase__ :       Any						=						"""https://www.google.com/recaptcha/api/siteverify"""

      # when method is not POST, direct user to login page
      if request.method != "POST":
            return render(lowerCAmelCase ,  """login.html"""						)

      # from the frontend, get username, password, and client_key
      UpperCAmelCase__ :       List[Any]						=						request.POST.get("""username"""						)
      UpperCAmelCase__ :       Optional[Any]						=						request.POST.get("""password"""						)
      UpperCAmelCase__ :       List[str]						=						request.POST.get("""g-recaptcha-response"""						)

      # post recaptcha response to Google's recaptcha api
      UpperCAmelCase__ :       Any						=						requests.post(lowerCAmelCase ,  data={"""secret""": secret_key, """response""": client_key}						)
      # if the recaptcha api verified our keys
      if response.json().get("""success""" ,  lowerCAmelCase						):
            # authenticate the user
            UpperCAmelCase__ :       List[str]						=						authenticate(lowerCAmelCase ,  username=lowerCAmelCase ,  password=lowerCAmelCase						)
            if user_in_database:
                  login(lowerCAmelCase ,  lowerCAmelCase						)
                  return redirect("""/your-webpage"""						)
      return render(lowerCAmelCase ,  """login.html"""						)

