


'''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_   :    Union[str, Any]				,   UpperCAmelCase_   :    int = 5_12							)					->		str:
     __lowerCamelCase       :     List[str]        =		x.shape[1] if len(x.shape							) > 1 else 1
     __lowerCamelCase       :     Optional[Any]        =		1 + np.arange(0				,   feature_num * offset				,   UpperCAmelCase_				,   dtype=np.intaa							)
     __lowerCamelCase       :     Optional[Any]        =		x + feature_offset
     return x



def       UpperCAmelCase__ (     UpperCAmelCase_   :    List[Any]				,   UpperCAmelCase_   :    Any=True							)					->		List[Any]:
     requires_backends(UpperCAmelCase_				,   ['cython']							)

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

     __lowerCamelCase       :     int        =		np.asarray(item['edge_index']				,   dtype=np.intaa							)

     __lowerCamelCase       :     Union[str, Any]        =		convert_to_single_emb(UpperCAmelCase_							) + 1
     __lowerCamelCase       :     Optional[int]        =		item['num_nodes']

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

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

     __lowerCamelCase    ,	__lowerCamelCase       :     List[str]        =		algos_graphormer.floyd_warshall(UpperCAmelCase_							)
     __lowerCamelCase       :     Optional[Any]        =		np.amax(UpperCAmelCase_							)

     __lowerCamelCase       :     Optional[int]        =		algos_graphormer.gen_edge_input(UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_							)
     __lowerCamelCase       :     List[str]        =		np.zeros([num_nodes + 1, num_nodes + 1]				,   dtype=np.single							)  # with graph token

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

     return item





class        UpperCAmelCase_  :





    """simple docstring"""






    def __init__(							self      ,					SCREAMING_SNAKE_CASE_=20      ,					SCREAMING_SNAKE_CASE_=False     )    ->					Tuple:
         if not is_cython_available():
              raise ImportError('Graphormer preprocessing needs Cython (pyximport)'     )

         __lowerCamelCase       :     int        =		spatial_pos_max
         __lowerCamelCase       :     List[str]        =		on_the_fly_processing







    def __call__(							self      ,					SCREAMING_SNAKE_CASE_     )    ->					Dict[str, Any]:
         if self.on_the_fly_processing:
              __lowerCamelCase       :     Dict        =		[preprocess_item(SCREAMING_SNAKE_CASE_     ) for i in features]

         if not isinstance(features[0]      ,					SCREAMING_SNAKE_CASE_     ):
              __lowerCamelCase       :     List[str]        =		[vars(SCREAMING_SNAKE_CASE_     ) for f in features]
         __lowerCamelCase       :     str        =		{}

         __lowerCamelCase       :     int        =		max(len(i['input_nodes']     ) for i in features     )
         __lowerCamelCase       :     List[str]        =		len(features[0]['input_nodes'][0]     )
         __lowerCamelCase       :     List[str]        =		len(features[0]['attn_edge_type'][0][0]     )
         __lowerCamelCase       :     str        =		max(len(i['input_edges'][0][0]     ) for i in features     )
         __lowerCamelCase       :     Union[str, Any]        =		len(features[0]['input_edges'][0][0][0]     )
         __lowerCamelCase       :     Any        =		len(SCREAMING_SNAKE_CASE_     )

         __lowerCamelCase       :     List[str]        =		torch.zeros(SCREAMING_SNAKE_CASE_      ,					max_node_num + 1      ,					max_node_num + 1      ,					dtype=torch.float     )
         __lowerCamelCase       :     Union[str, Any]        =		torch.zeros(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					dtype=torch.long     )
         __lowerCamelCase       :     Tuple        =		torch.zeros(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					dtype=torch.long     )
         __lowerCamelCase       :     Any        =		torch.zeros(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					dtype=torch.long     )
         __lowerCamelCase       :     int        =		torch.zeros(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					dtype=torch.long     )
         __lowerCamelCase       :     Optional[int]        =		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       :     Union[str, Any]        =		torch.tensor(f[k]     )

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

              __lowerCamelCase       :     Optional[Any]        =		f['attn_bias']
              __lowerCamelCase       :     List[Any]        =		f[
                  'attn_edge_type'
              ]
              __lowerCamelCase       :     str        =		f['spatial_pos']
              __lowerCamelCase       :     Any        =		f['in_degree']
              __lowerCamelCase       :     Any        =		f['input_nodes']
              __lowerCamelCase       :     int        =		f['input_edges']

         __lowerCamelCase       :     str        =		batch['in_degree']

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

         return batch










'''simple docstring'''



import torch

from transformers import AutoTokenizer





class        UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    def __init__(							self      ,					SCREAMING_SNAKE_CASE_     )    ->					int:
         __lowerCamelCase       :     Dict        =		AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_     )







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_     )    ->					int:

         if isinstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     ) and all(isinstance(_x      ,					SCREAMING_SNAKE_CASE_     ) for _x in x     ):
              __lowerCamelCase       :     Union[str, Any]        =		None
              for l in x:
                   __lowerCamelCase       :     Tuple        =		self.tokenizer(
                       SCREAMING_SNAKE_CASE_      ,					padding='max_length'      ,					max_length=3_84      ,					truncation=SCREAMING_SNAKE_CASE_      ,					return_tensors='pt'      ,					)
                   __lowerCamelCase       :     str        =		torch.tensor([len(SCREAMING_SNAKE_CASE_     )]     )
                   if d is not None:
                        for k in d.keys():
                             __lowerCamelCase       :     List[Any]        =		torch.cat((d[k], t[k])      ,					0     )
                   else:
                        __lowerCamelCase       :     str        =		t

              __lowerCamelCase       :     List[Any]        =		torch.tensor(self.tokenizer.convert_tokens_to_ids('[E]'     )     )
              __lowerCamelCase       :     Tuple        =		torch.tensor(self.tokenizer.convert_tokens_to_ids('[/E]'     )     )

         elif isinstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     ) and all(isinstance(_x      ,					SCREAMING_SNAKE_CASE_     ) for _x in x     ):
              __lowerCamelCase       :     Optional[Any]        =		self.tokenizer(
                  SCREAMING_SNAKE_CASE_      ,					padding='max_length'      ,					max_length=3_84      ,					truncation=SCREAMING_SNAKE_CASE_      ,					return_tensors='pt'      ,					)

         else:
              raise Exception(
                  'Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'
                  ' strings` for supports are supported.'     )

         return d







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_=0.7_0     )    ->					str:
         __lowerCamelCase       :     str        =		[]
         for idx in range(len(W_query['input_ids']     )     ):
              __lowerCamelCase       :     Optional[Any]        =		range(p_start.shape[1]     )

              __lowerCamelCase       :     List[Any]        =		[]
              for start_id in start_indexes:
                   for end_id in end_indexes:
                        if start_id < end_id:
                             output.append(
                                 (
                                     start_id,
                                     end_id,
                                     p_start[idx][start_id].item(),
                                     p_end[idx][end_id].item(),
                                 )     )

              output.sort(key=lambda SCREAMING_SNAKE_CASE_     : (tup[2] * tup[3])      ,					reverse=SCREAMING_SNAKE_CASE_     )
              __lowerCamelCase       :     str        =		[]
              for k in range(len(SCREAMING_SNAKE_CASE_     )     ):
                   if output[k][2] * output[k][3] >= thresh:
                        __lowerCamelCase    ,	__lowerCamelCase       :     Union[str, Any]        =		output[k][0], output[k][1]
                        __lowerCamelCase       :     List[str]        =		self.tokenizer.decode(W_query['input_ids'][idx][c_start_pos:c_end_pos]     )
                        temp.append((decoded, output[k][2] * output[k][3])     )

              final_outputs.append(SCREAMING_SNAKE_CASE_     )

         return final_outputs






