'''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_							(      _UpperCAmelCase						:   int    ,			_UpperCAmelCase						:   int = 512					)					->					List[str]:


       """simple docstring"""







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

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Tuple    ,			_UpperCAmelCase						:   Any=True					)					->					Any:


       """simple docstring"""







       requires_backends(_UpperCAmelCase    ,			["cython"]					)

       if keep_features and "edge_attr" in item.keys():  # edge_attr
              _UpperCAmelCase  :						Optional[int]							   =					np.asarray(item["edge_attr"]    ,			dtype=np.intaa					)
       else:
              _UpperCAmelCase  :						List[str]							   =					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  :						Optional[Any]							   =					np.asarray(item["node_feat"]    ,			dtype=np.intaa					)
       else:
              _UpperCAmelCase  :						Dict							   =					np.ones((item["num_nodes"], 1)    ,			dtype=np.intaa					)  # same embedding for all

       _UpperCAmelCase  :						Optional[int]							   =					np.asarray(item["edge_index"]    ,			dtype=np.intaa					)

       _UpperCAmelCase  :						Union[str, Any]							   =					convert_to_single_emb(_UpperCAmelCase					) + 1
       _UpperCAmelCase  :						int							   =					item["num_nodes"]

       if len(edge_attr.shape					) == 1:
              _UpperCAmelCase  :						Union[str, Any]							   =					edge_attr[:, None]
       _UpperCAmelCase  :						int							   =					np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]    ,			dtype=np.intaa					)
       _UpperCAmelCase  :						Optional[int]							   =					convert_to_single_emb(_UpperCAmelCase					) + 1

       # node adj matrix [num_nodes, num_nodes] bool
       _UpperCAmelCase  :						int							   =					np.zeros([num_nodes, num_nodes]    ,			dtype=_UpperCAmelCase					)
       _UpperCAmelCase  :						Dict							   =					True

       _UpperCAmelCase   ,						_UpperCAmelCase  :						Union[str, Any]							   =					algos_graphormer.floyd_warshall(_UpperCAmelCase					)
       _UpperCAmelCase  :						List[Any]							   =					np.amax(_UpperCAmelCase					)

       _UpperCAmelCase  :						List[Any]							   =					algos_graphormer.gen_edge_input(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase					)
       _UpperCAmelCase  :						List[Any]							   =					np.zeros([num_nodes + 1, num_nodes + 1]    ,			dtype=np.single					)  # with graph token

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

       return item


class 			lowerCamelCase_			:
      '''simple docstring'''

      def __init__( self		:       str		,      A		:       Any=20		,      A		:       Optional[Any]=False ):
             if not is_cython_available():
                    raise ImportError("Graphormer preprocessing needs Cython (pyximport)" )

             _UpperCAmelCase  :						List[str]							   =					spatial_pos_max
             _UpperCAmelCase  :						Union[str, Any]							   =					on_the_fly_processing





      def __call__( self		:       str		,      A		:       List[dict] ):
             if self.on_the_fly_processing:
                    _UpperCAmelCase  :						List[str]							   =					[preprocess_item(A ) for i in features]

             if not isinstance(features[0]		,      A ):
                    _UpperCAmelCase  :						str							   =					[vars(A ) for f in features]
             _UpperCAmelCase  :						Any							   =					{}

             _UpperCAmelCase  :						int							   =					max(len(i["input_nodes"] ) for i in features )
             _UpperCAmelCase  :						Optional[int]							   =					len(features[0]["input_nodes"][0] )
             _UpperCAmelCase  :						Dict							   =					len(features[0]["attn_edge_type"][0][0] )
             _UpperCAmelCase  :						Optional[int]							   =					max(len(i["input_edges"][0][0] ) for i in features )
             _UpperCAmelCase  :						Optional[int]							   =					len(features[0]["input_edges"][0][0][0] )
             _UpperCAmelCase  :						Any							   =					len(A )

             _UpperCAmelCase  :						Dict							   =					torch.zeros(A		,      max_node_num + 1		,      max_node_num + 1		,      dtype=torch.float )
             _UpperCAmelCase  :						List[str]							   =					torch.zeros(A		,      A		,      A		,      A		,      dtype=torch.long )
             _UpperCAmelCase  :						Any							   =					torch.zeros(A		,      A		,      A		,      dtype=torch.long )
             _UpperCAmelCase  :						Union[str, Any]							   =					torch.zeros(A		,      A		,      dtype=torch.long )
             _UpperCAmelCase  :						int							   =					torch.zeros(A		,      A		,      A		,      dtype=torch.long )
             _UpperCAmelCase  :						Optional[int]							   =					torch.zeros(
                 A		,      A		,      A		,      A		,      A		,      dtype=torch.long )

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

                    if len(f["attn_bias"][1:, 1:][f["spatial_pos"] >= self.spatial_pos_max] ) > 0:
                           _UpperCAmelCase  :						Optional[int]							   =					float("-inf" )

                    _UpperCAmelCase  :						Any							   =					f["attn_bias"]
                    _UpperCAmelCase  :						List[str]							   =					f[
                        "attn_edge_type"
                    ]
                    _UpperCAmelCase  :						str							   =					f["spatial_pos"]
                    _UpperCAmelCase  :						List[Any]							   =					f["in_degree"]
                    _UpperCAmelCase  :						Any							   =					f["input_nodes"]
                    _UpperCAmelCase  :						Union[str, Any]							   =					f["input_edges"]

             _UpperCAmelCase  :						Dict							   =					batch["in_degree"]

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

             return batch


'''simple docstring'''




import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


__SCREAMING_SNAKE_CASE :  Any  =						TypeVar("""T""", bound="""TaskTemplate""")

@dataclass(frozen=snake_case__					)
class 			lowerCamelCase_			(abc.ABC					):
      '''simple docstring'''

      __UpperCamelCase:						str
      __UpperCamelCase:						ClassVar[Features]
      __UpperCamelCase:						ClassVar[Features]




      def   _A			( self		:       T		,      A		:       Features ):
             return copy.deepcopy(self )




      @property
      def   _A			( self		:       List[str] ):
             return Features(**self.input_schema		,      **self.label_schema )




      @property
      @abc.abstractmethod
      def   _A			( self		:       Optional[int] ):
             raise NotImplementedError





      @classmethod
      def   _A			( cls		:       Type[T]		,      A		:       dict ):
             _UpperCAmelCase  :						Optional[Any]							   =					{f.name for f in dataclasses.fields(cls )}
             return cls(**{k: v for k, v in template_dict.items() if k in field_names} )

