





'''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						(						lowercase					,	lowercase = 512	)					->					Tuple:
			__lowerCAmelCase											=				x.shape[1] if len(x.shape	) > 1 else 1
			__lowerCAmelCase											=				1 + np.arange(0					,	feature_num * offset					,	lowercase					,	dtype=np.intaa	)
			__lowerCAmelCase											=				x + feature_offset
			return x





def    _lowerCAmelCase						(						lowercase					,	lowercase=True	)					->					Optional[int]:
			requires_backends(lowercase					,	["""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(lowercase	) + 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(lowercase	) + 1

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

			__lowerCAmelCase						,       __lowerCAmelCase											=				algos_graphormer.floyd_warshall(lowercase	)
			__lowerCAmelCase											=				np.amax(lowercase	)

			__lowerCAmelCase											=				algos_graphormer.gen_edge_input(lowercase					,	lowercase					,	lowercase	)
			__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(lowercase					,	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     _UpperCAmelCase							:
		def __init__(  self,__SCREAMING_SNAKE_CASE=20,__SCREAMING_SNAKE_CASE=False			):



					'''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			):



					'''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








'''simple docstring'''







from collections import namedtuple

_a					: Any      =    namedtuple("""from_to""", """from_ to""")

_a					: Dict      =    {
    """millimeter""": """mm""",
    """centimeter""": """cm""",
    """meter""": """m""",
    """kilometer""": """km""",
    """inch""": """in""",
    """inche""": """in""",  # Trailing 's' has been stripped off
    """feet""": """ft""",
    """foot""": """ft""",
    """yard""": """yd""",
    """mile""": """mi""",
}

_a					: Optional[Any]      =    {
    """mm""": from_to(0.001, 1_0_0_0),
    """cm""": from_to(0.01, 1_0_0),
    """m""": from_to(1, 1),
    """km""": from_to(1_0_0_0, 0.001),
    """in""": from_to(0.0254, 39.3701),
    """ft""": from_to(0.3048, 3.28084),
    """yd""": from_to(0.9144, 1.09361),
    """mi""": from_to(1609.34, 0.000621371),
}





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase	)					->					float:
			__lowerCAmelCase											=				from_type.lower().rstrip("""s"""	)
			__lowerCAmelCase											=				TYPE_CONVERSION.get(lowercase					,	lowercase	)
			__lowerCAmelCase											=				to_type.lower().rstrip("""s"""	)
			__lowerCAmelCase											=				TYPE_CONVERSION.get(lowercase					,	lowercase	)
			if new_from not in METRIC_CONVERSION:
						__lowerCAmelCase											=				(
						    f'Invalid \'from_type\' value: {from_type!r}.\n'
						    f'Conversion abbreviations are: {", ".join(lowercase	)}'
						)
						raise ValueError(lowercase	)
			if new_to not in METRIC_CONVERSION:
						__lowerCAmelCase											=				(
						    f'Invalid \'to_type\' value: {to_type!r}.\n'
						    f'Conversion abbreviations are: {", ".join(lowercase	)}'
						)
						raise ValueError(lowercase	)
			return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
	import doctest

	doctest.testmod()

