






"""simple docstring"""



# Lint as: python3

import hashlib
from typing import Union




def     lowerCamelCase_					(	__A	:		Union[str, int, bytes]      ):
					if isinstance(__A      ,						__A      ):
										# Data already in bytes, returns as it as
										return hash_data
					elif isinstance(__A      ,						__A      ):
										# We keep the data as it as for it ot be later encoded to UTF-8
										# However replace `\\` with `/` for Windows compatibility
										lowerCamelCase__					=hash_data.replace('\\'      ,						'/'      )
					elif isinstance(__A      ,						__A      ):
										lowerCamelCase__					=str(__A      )
					else:
										# If data is not of the required type, raise error
										raise InvalidKeyError(__A      )

					return hash_data.encode('utf-8'      )




class 							__lowercase(							_UpperCamelCase	):






						def __init__(       self     ,    _SCREAMING_SNAKE_CASE)      ->	Optional[int]:




											"""simple docstring"""





											lowerCamelCase__					='\nFAILURE TO GENERATE DATASET: Invalid key type detected'
											lowerCamelCase__					=F"\nFound Key {hash_data} of type {type(_SCREAMING_SNAKE_CASE)}"
											lowerCamelCase__					='\nKeys should be either str, int or bytes type'
											super().__init__(F"{self.prefix}{self.err_msg}{self.suffix}")




class 							__lowercase(							_UpperCamelCase	):






						def __init__(       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE="")      ->	Union[str, Any]:




											"""simple docstring"""





											lowerCamelCase__					=key
											lowerCamelCase__					=duplicate_key_indices
											lowerCamelCase__					=fix_msg
											lowerCamelCase__					='Found multiple examples generated with the same key'
											if len(_SCREAMING_SNAKE_CASE) <= 20:
																lowerCamelCase__					=F"\nThe examples at index {', '.join(_SCREAMING_SNAKE_CASE)} have the key {key}"
											else:
																lowerCamelCase__					=F"\nThe examples at index {', '.join(duplicate_key_indices[:20])}... ({len(_SCREAMING_SNAKE_CASE) - 20} more) have the key {key}"
											lowerCamelCase__					='\n' + fix_msg if fix_msg else ''
											super().__init__(F"{self.prefix}{self.err_msg}{self.suffix}")




class 							__lowercase:






						def __init__(       self     ,    _SCREAMING_SNAKE_CASE)      ->	Union[str, Any]:




											"""simple docstring"""





											lowerCamelCase__					=hashlib.mda(_as_bytes(_SCREAMING_SNAKE_CASE))






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE)      ->	int:




											"""simple docstring"""





											lowerCamelCase__					=self._split_mda.copy()
											lowerCamelCase__					=_as_bytes(_SCREAMING_SNAKE_CASE)
											mda.update(_SCREAMING_SNAKE_CASE)
											# Convert to integer with hexadecimal conversion
											return int(mda.hexdigest()     ,    16)







"""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     lowerCamelCase_					(	__A	:		List[str]      ,						__A	:		int = 512      ):
					lowerCamelCase__					=x.shape[1] if len(x.shape      ) > 1 else 1
					lowerCamelCase__					=1 + np.arange(0      ,						feature_num * offset      ,						__A      ,						dtype=np.intaa      )
					lowerCamelCase__					=x + feature_offset
					return x




def     lowerCamelCase_					(	__A	:		str      ,						__A	:		Optional[Any]=True      ):
					requires_backends(__A      ,						['cython']      )

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

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

					lowerCamelCase__					=convert_to_single_emb(__A      ) + 1
					lowerCamelCase__					=item['num_nodes']

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

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

					lowerCamelCase__					,  lowerCamelCase__					=algos_graphormer.floyd_warshall(__A      )
					lowerCamelCase__					=np.amax(__A      )

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

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

					return item




class 							__lowercase:






						def __init__(       self     ,    _SCREAMING_SNAKE_CASE=20     ,    _SCREAMING_SNAKE_CASE=False)      ->	Any:




											"""simple docstring"""





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

											lowerCamelCase__					=spatial_pos_max
											lowerCamelCase__					=on_the_fly_processing






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




											"""simple docstring"""





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

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

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

											lowerCamelCase__					=torch.zeros(_SCREAMING_SNAKE_CASE     ,    max_node_num + 1     ,    max_node_num + 1     ,    dtype=torch.float)
											lowerCamelCase__					=torch.zeros(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    dtype=torch.long)
											lowerCamelCase__					=torch.zeros(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    dtype=torch.long)
											lowerCamelCase__					=torch.zeros(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    dtype=torch.long)
											lowerCamelCase__					=torch.zeros(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    dtype=torch.long)
											lowerCamelCase__					=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"]:
																					lowerCamelCase__					=torch.tensor(f[k])

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

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

											lowerCamelCase__					=batch['in_degree']

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

											return batch