
"""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 						__magic_name__		(				UpperCamelCase    :		int       ,		UpperCamelCase    :		int = 512    )    ->	Optional[Any]:

    '''simple docstring'''





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



def 						__magic_name__		(				UpperCamelCase    :		str       ,		UpperCamelCase    :		Optional[Any]=True    )    ->	Optional[Any]:

    '''simple docstring'''





    requires_backends(lowercase_       ,		['cython']    )

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

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

    a__    = convert_to_single_emb(lowercase_    ) + 1
    a__    = item['num_nodes']

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

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

    a__    = algos_graphormer.floyd_warshall(lowercase_    )
    a__    = np.amax(lowercase_    )

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

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

    return item







class 						lowercase:



     def __init__(       self    ,							__SCREAMING_SNAKE_CASE=2_0    ,							__SCREAMING_SNAKE_CASE=False      )		->    str:







         """simple docstring"""



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

         a__    = spatial_pos_max
         a__    = on_the_fly_processing




     def __call__(       self    ,							__SCREAMING_SNAKE_CASE      )		->    Dict[str, Any]:







         """simple docstring"""



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

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

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

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

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

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

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

         a__    = batch['in_degree']

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

         return batch




"""simple docstring"""




import shutil

import requests



def 						__magic_name__		(				UpperCamelCase    :		str       ,		UpperCamelCase    :		bool = False       ,		UpperCamelCase    :		str = "."    )    ->	dict:
    a__    = 'https://api.nasa.gov/planetary/apod'
    return requests.get(UpperCamelCase       ,		params={'api_key': api_key}    ).json()



def 						__magic_name__		(				UpperCamelCase    :		str       ,		UpperCamelCase    :		str = "."    )    ->	dict:
    a__    = get_apod_data(UpperCamelCase    )
    a__    = apod_data['url']
    a__    = img_url.split('/'    )[-1]
    a__    = requests.get(UpperCamelCase       ,		stream=UpperCamelCase    )

    with open(f'{path}/{img_name}'       ,		'wb+'    ) as img_file:
        shutil.copyfileobj(response.raw       ,		UpperCamelCase    )
    del response
    return apod_data



def 						__magic_name__		(				UpperCamelCase    :		str    )    ->	dict:
    a__    = 'https://images-api.nasa.gov/search'
    return requests.get(UpperCamelCase       ,		params={'q': query}    ).json()


if __name__ == "__main__":
    print(save_apod('YOUR API KEY'))
    a		:					List[Any]    	=					get_archive_data('apollo 2011')['collection']['items']
    print(apollo_aaaa_items[0]['data'][0]['description'])


