





"""simple docstring"""
import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union
class lowerCamelCase__  (    enum.Enum				):
			__UpperCAmelCase        =					"""timm"""
			__UpperCAmelCase        =					"""transformers"""




def 			__snake_case						(      __A ,__A ,__A    )		->				List[str]:
			if stage_names is None:
						raise ValueError("""Stage_names must be set for transformers backbones"""    )

			if out_features is not None:
						if not isinstance(__A ,(list,)    ):
									raise ValueError(F'''out_features must be a list {type(__A    )}'''    )
						if any(feat not in stage_names for feat in out_features    ):
									raise ValueError(F'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''    )

			if out_indices is not None:
						if not isinstance(__A ,(list, tuple)    ):
									raise ValueError(F'''out_indices must be a list or tuple, got {type(__A    )}'''    )
						if any(idx >= len(__A    ) for idx in out_indices    ):
									raise ValueError("""out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"""    )

			if out_features is not None and out_indices is not None:
						if len(__A    ) != len(__A    ):
									raise ValueError("""out_features and out_indices should have the same length if both are set"""    )
						if out_features != [stage_names[idx] for idx in out_indices]:
									raise ValueError("""out_features and out_indices should correspond to the same stages if both are set"""    )




def 			__snake_case						(      __A ,__A ,__A ,)		->				str:
			if out_indices is None and out_features is None:
						lowercase    :			str  =       [len(__A    ) - 1]
						lowercase    :			Tuple  =       [stage_names[-1]]
			elif out_indices is None and out_features is not None:
						lowercase    :			List[Any]  =       [stage_names.index(__A    ) for layer in out_features]
			elif out_features is None and out_indices is not None:
						lowercase    :			Dict  =       [stage_names[idx] for idx in out_indices]
			return out_features, out_indices




def 			__snake_case						(      __A ,__A ,__A ,)		->				Tuple[List[str], List[int]]:
			verify_out_features_out_indices(out_features=__A ,out_indices=__A ,stage_names=__A    )
			lowercase    :			List[Any]  =       _align_output_features_output_indices(
			    out_features=__A ,out_indices=__A ,stage_names=__A    )
			# Verify that the aligned out_features and out_indices are valid
			verify_out_features_out_indices(out_features=__A ,out_indices=__A ,stage_names=__A    )
			return output_features, output_indices
class lowerCamelCase__  :
			__UpperCAmelCase        =					None



			def    _UpperCAmelCase	(       self      ,   snake_case	) ->    None:







						"""simple docstring"""
						if getattr(self      ,   """_backbone"""      ,   snake_case	) is None:
									raise ValueError("""self._backbone must be set before calling _init_timm_backbone"""	)

						# These will diagree with the defaults for the transformers models e.g. for resnet50
						# the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
						# the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
						lowercase    :			Union[str, Any]  =       [stage["""module"""] for stage in self._backbone.feature_info.info]
						lowercase    :			Dict  =       [stage["""num_chs"""] for stage in self._backbone.feature_info.info]
						lowercase    :			Union[str, Any]  =       self._backbone.feature_info.out_indices
						lowercase    :			Tuple  =       self._backbone.feature_info.module_name()

						# We verify the out indices and out features are valid
						verify_out_features_out_indices(
						    out_features=snake_case      ,   out_indices=snake_case      ,   stage_names=self.stage_names	)
						lowercase    :			Optional[int]  =       out_features, out_indices



			def    _UpperCAmelCase	(       self      ,   snake_case	) ->    None:







						"""simple docstring"""
						lowercase    :			str  =       getattr(snake_case      ,   """stage_names"""	)
						lowercase    :			List[str]  =       getattr(snake_case      ,   """out_features"""      ,   snake_case	)
						lowercase    :			Optional[int]  =       getattr(snake_case      ,   """out_indices"""      ,   snake_case	)

						lowercase    :			Dict  =       stage_names
						lowercase    :			Tuple  =       get_aligned_output_features_output_indices(
						    out_features=snake_case      ,   out_indices=snake_case      ,   stage_names=snake_case	)
						# Number of channels for each stage. This is set in the transformer backbone model init
						lowercase    :			Optional[int]  =       None



			def    _UpperCAmelCase	(       self      ,   snake_case	) ->    None:







						"""simple docstring"""
						lowercase    :			Optional[Any]  =       config

						lowercase    :			Optional[Any]  =       getattr(snake_case      ,   """use_timm_backbone"""      ,   snake_case	)
						lowercase    :			Optional[int]  =       BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

						if self.backbone_type == BackboneType.TIMM:
									self._init_timm_backbone(snake_case	)
						elif self.backbone_type == BackboneType.TRANSFORMERS:
									self._init_transformers_backbone(snake_case	)
						else:
									raise ValueError(f'''backbone_type {self.backbone_type} not supported.'''	)



			@property
			def    _UpperCAmelCase	(       self	) ->    Dict:







						"""simple docstring"""
						return self._out_features



			@out_features.setter
			def    _UpperCAmelCase	(       self      ,   snake_case	) ->    Dict:







						"""simple docstring"""
						lowercase    :			Tuple  =       get_aligned_output_features_output_indices(
						    out_features=snake_case      ,   out_indices=snake_case      ,   stage_names=self.stage_names	)



			@property
			def    _UpperCAmelCase	(       self	) ->    str:







						"""simple docstring"""
						return self._out_indices



			@out_indices.setter
			def    _UpperCAmelCase	(       self      ,   snake_case	) ->    List[Any]:







						"""simple docstring"""
						lowercase    :			int  =       get_aligned_output_features_output_indices(
						    out_features=snake_case      ,   out_indices=snake_case      ,   stage_names=self.stage_names	)



			@property
			def    _UpperCAmelCase	(       self	) ->    Any:







						"""simple docstring"""
						return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names	)}



			@property
			def    _UpperCAmelCase	(       self	) ->    List[str]:







						"""simple docstring"""
						return [self.out_feature_channels[name] for name in self.out_features]



			def    _UpperCAmelCase	(       self      ,   *snake_case      ,   **snake_case	) ->    Optional[Any]:







						"""simple docstring"""
						lowercase    :			Tuple  =       dict(inspect.signature(self.forward	).parameters	)
						lowercase    :			Optional[int]  =       {k: v for k, v in kwargs.items() if k in signature}
						return self(*snake_case      ,   **snake_case	)



			def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   ) ->    Optional[int]:







						"""simple docstring"""
						raise NotImplementedError("""This method should be implemented by the derived class."""	)





			def    _UpperCAmelCase	(       self	) ->    str:







						"""simple docstring"""
						lowercase    :			List[Any]  =       super().to_dict()
						lowercase    :			Union[str, Any]  =       output.pop("""_out_features"""	)
						lowercase    :			Optional[Any]  =       output.pop("""_out_indices"""	)
						return output




class lowerCamelCase__  :



			@property
			def    _UpperCAmelCase	(       self	) ->    Tuple:







						"""simple docstring"""
						return self._out_features



			@out_features.setter
			def    _UpperCAmelCase	(       self      ,   snake_case	) ->    Tuple:







						"""simple docstring"""
						lowercase    :			str  =       get_aligned_output_features_output_indices(
						    out_features=snake_case      ,   out_indices=snake_case      ,   stage_names=self.stage_names	)



			@property
			def    _UpperCAmelCase	(       self	) ->    Optional[int]:







						"""simple docstring"""
						return self._out_indices



			@out_indices.setter
			def    _UpperCAmelCase	(       self      ,   snake_case	) ->    int:







						"""simple docstring"""
						lowercase    :			str  =       get_aligned_output_features_output_indices(
						    out_features=snake_case      ,   out_indices=snake_case      ,   stage_names=self.stage_names	)





			def    _UpperCAmelCase	(       self	) ->    Any:







						"""simple docstring"""
						lowercase    :			Dict  =       super().to_dict()
						lowercase    :			int  =       output.pop("""_out_features"""	)
						lowercase    :			List[str]  =       output.pop("""_out_indices"""	)
						return output









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




def 			__snake_case						(      __A ,__A=True    )		->				List[Any]:
   requires_backends(__A ,["""cython"""]    )

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

   lowercase    :			str  =       np.asarray(item["""edge_index"""] ,dtype=np.intaa    )

   lowercase    :			Tuple  =       convert_to_single_emb(__A    ) + 1
   lowercase    :			Dict  =       item["""num_nodes"""]

   if len(edge_attr.shape    ) == 1:
      lowercase    :			Dict  =       edge_attr[:, None]
   lowercase    :			Tuple  =       np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]] ,dtype=np.intaa    )
   lowercase    :			List[Any]  =       convert_to_single_emb(__A    ) + 1

   # node adj matrix [num_nodes, num_nodes] bool
   lowercase    :			List[Any]  =       np.zeros([num_nodes, num_nodes] ,dtype=__A    )
   lowercase    :			str  =       True

   lowercase  ,					lowercase    :			List[str]  =       algos_graphormer.floyd_warshall(__A    )
   lowercase    :			str  =       np.amax(__A    )

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

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

   return item







class lowerCamelCase__  :



   def __init__(       self      ,   snake_case=2_0      ,   snake_case=False	) ->    List[Any]:







      """simple docstring"""
      if not is_cython_available():
         raise ImportError("""Graphormer preprocessing needs Cython (pyximport)"""	)

      lowercase    :			Any  =       spatial_pos_max
      lowercase    :			Dict  =       on_the_fly_processing





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







      """simple docstring"""
      if self.on_the_fly_processing:
         lowercase    :			Dict  =       [preprocess_item(snake_case	) for i in features]

      if not isinstance(features[0]      ,   snake_case	):
         lowercase    :			Optional[int]  =       [vars(snake_case	) for f in features]
      lowercase    :			Any  =       {}

      lowercase    :			Any  =       max(len(i["""input_nodes"""]	) for i in features	)
      lowercase    :			List[str]  =       len(features[0]["""input_nodes"""][0]	)
      lowercase    :			Tuple  =       len(features[0]["""attn_edge_type"""][0][0]	)
      lowercase    :			str  =       max(len(i["""input_edges"""][0][0]	) for i in features	)
      lowercase    :			Dict  =       len(features[0]["""input_edges"""][0][0][0]	)
      lowercase    :			Union[str, Any]  =       len(snake_case	)

      lowercase    :			List[str]  =       torch.zeros(snake_case      ,   max_node_num + 1      ,   max_node_num + 1      ,   dtype=torch.float	)
      lowercase    :			List[Any]  =       torch.zeros(snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   dtype=torch.long	)
      lowercase    :			int  =       torch.zeros(snake_case      ,   snake_case      ,   snake_case      ,   dtype=torch.long	)
      lowercase    :			List[str]  =       torch.zeros(snake_case      ,   snake_case      ,   dtype=torch.long	)
      lowercase    :			Tuple  =       torch.zeros(snake_case      ,   snake_case      ,   snake_case      ,   dtype=torch.long	)
      lowercase    :			Dict  =       torch.zeros(
          snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   dtype=torch.long	)

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

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

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

      lowercase    :			Dict  =       batch["""in_degree"""]

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

      return batch


