

'''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							(	lowercase__						:  List[Any],					lowercase__						:  int = 5_12 ):






     '''simple docstring'''





     __lowercase					=x.shape[1] if len(x.shape ) > 1 else 1
     __lowercase					=1 + np.arange(0,					feature_num * offset,					lowercase__,					dtype=np.intaa )
     __lowercase					=x + feature_offset
     return x

def 				__UpperCamelCase							(	lowercase__						:  Dict,					lowercase__						:  Optional[Any]=True ):






     '''simple docstring'''





     requires_backends(lowercase__,					['cython'] )

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

     __lowercase					=np.asarray(item['edge_index'],					dtype=np.intaa )

     __lowercase					=convert_to_single_emb(lowercase__ ) + 1
     __lowercase					=item['num_nodes']

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

     # node adj matrix [num_nodes, num_nodes] bool
     __lowercase					=np.zeros([num_nodes, num_nodes],					dtype=lowercase__ )
     __lowercase					=True

     __lowercase		,     __lowercase					=algos_graphormer.floyd_warshall(lowercase__ )
     __lowercase					=np.amax(lowercase__ )

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

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

     return item







class   lowerCAmelCase     :







       def __init__(							self			: str						,      __lowercase			: List[Any]=20						,      __lowercase			: str=False      ):







            """simple docstring"""







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

            __lowercase					=spatial_pos_max
            __lowercase					=on_the_fly_processing







       def __call__(							self			: Any						,      __lowercase			: List[dict]      ):







            """simple docstring"""







            if self.on_the_fly_processing:
                 __lowercase					=[preprocess_item(__lowercase      ) for i in features]

            if not isinstance(features[0]						,      __lowercase      ):
                 __lowercase					=[vars(__lowercase      ) for f in features]
            __lowercase					={}

            __lowercase					=max(len(i['input_nodes']      ) for i in features      )
            __lowercase					=len(features[0]['input_nodes'][0]      )
            __lowercase					=len(features[0]['attn_edge_type'][0][0]      )
            __lowercase					=max(len(i['input_edges'][0][0]      ) for i in features      )
            __lowercase					=len(features[0]['input_edges'][0][0][0]      )
            __lowercase					=len(__lowercase      )

            __lowercase					=torch.zeros(__lowercase						,      max_node_num + 1						,      max_node_num + 1						,      dtype=torch.float      )
            __lowercase					=torch.zeros(__lowercase						,      __lowercase						,      __lowercase						,      __lowercase						,      dtype=torch.long      )
            __lowercase					=torch.zeros(__lowercase						,      __lowercase						,      __lowercase						,      dtype=torch.long      )
            __lowercase					=torch.zeros(__lowercase						,      __lowercase						,      dtype=torch.long      )
            __lowercase					=torch.zeros(__lowercase						,      __lowercase						,      __lowercase						,      dtype=torch.long      )
            __lowercase					=torch.zeros(
                __lowercase						,      __lowercase						,      __lowercase						,      __lowercase						,      __lowercase						,      dtype=torch.long      )

            for ix, f in enumerate(__lowercase      ):
                 for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
                      __lowercase					=torch.tensor(f[k]      )

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

                 __lowercase					=f['attn_bias']
                 __lowercase					=f[
                     'attn_edge_type'
                 ]
                 __lowercase					=f['spatial_pos']
                 __lowercase					=f['in_degree']
                 __lowercase					=f['input_nodes']
                 __lowercase					=f['input_edges']

            __lowercase					=batch['in_degree']

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

            return batch





'''simple docstring'''

import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCAmelCase				   =  {'''UserAgent''': UserAgent().random}
UpperCAmelCase				   =  '''https://www.mywaifulist.moe/random'''

def 				__UpperCamelCase							(	lowercase__						:  str,					lowercase__						:  str ):






     '''simple docstring'''





     __lowercase					=requests.get(lowercase__,					headers=lowercase__ )
     with open(lowercase__,					'wb' ) as file:
          file.write(image.content )

def 				__UpperCamelCase							(	):






     '''simple docstring'''





     __lowercase					=BeautifulSoup(requests.get(lowercase__,					headers=lowercase__ ).text,					'html.parser' )
     __lowercase					=soup.find('meta',					attrs={'property': 'og:title'} ).attrs['content']
     __lowercase					=soup.find('meta',					attrs={'property': 'og:image'} ).attrs['content']
     __lowercase					=soup.find('p',					id='description' ).get_text()
     __lowercase		,     __lowercase					=os.path.splitext(os.path.basename(lowercase__ ) )
     __lowercase					=title.strip().replace(' ',					'_' )
     __lowercase					=F'''{image_title}{image_extension}'''
     save_image(lowercase__,					lowercase__ )
     return (title, description, image_title)


if __name__ == "__main__":
       UpperCAmelCase   ,			UpperCAmelCase   ,			UpperCAmelCase				   =  random_anime_character()
       print(F'''{title}\n\n{desc}\n\nImage saved : {image_title}''')


