





"""simple docstring"""
import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast




def 			__snake_case						(      __A ,__A=100 ,__A=" "    )		->				str:
			lowercase    :			Optional[int]  =       text.split(__A    )
			return [character.join(text[i : i + n]    ).strip() for i in range(0 ,len(__A    ) ,__A    )]




def 			__snake_case						(      __A    )		->				int:
			lowercase  ,					lowercase    :			Union[str, Any]  =       [], []
			for title, text in zip(documents["""title"""] ,documents["""text"""]    ):
						if text is not None:
									for passage in split_text(__A    ):
												titles.append(title if title is not None else """"""    )
												texts.append(__A    )
			return {"title": titles, "text": texts}




def 			__snake_case						(      __A ,__A ,__A ,__A ,__A ,__A    )		->				int:
			lowercase    :			Optional[Any]  =       load_dataset(
			    """csv""" ,data_files=[csv_path] ,split="""train""" ,delimiter="""\t""" ,column_names=["""title""", """text"""]    )
			lowercase    :			Any  =       kb_dataset.map(
			    __A ,batched=__A ,num_proc=1    )  # if you want you can load already splitted csv.
			lowercase    :			str  =       [kb_dataset.shard(__A ,__A ,contiguous=__A    ) for i in range(__A    )]
			lowercase    :			Optional[Any]  =       kb_list[process_num]

			lowercase    :			Optional[int]  =       """data_""" + str(__A    )
			lowercase    :			List[str]  =       os.path.join(__A ,__A    )

			lowercase    :			str  =       DPRContextEncoderTokenizerFast.from_pretrained("""facebook/dpr-ctx_encoder-multiset-base"""    )
			lowercase    :			int  =       ctx_encoder.to(device=__A    )

			def embed(
			    __A ,__A ,__A ,__A    ) -> dict:
						lowercase    :			Dict  =       ctx_tokenizer(
						    documents["""title"""] ,documents["""text"""] ,truncation=__A ,padding="""longest""" ,return_tensors="""pt"""    )["""input_ids"""]
						lowercase    :			Dict  =       ctx_encoder(input_ids.to(device=__A    ) ,return_dict=__A    ).pooler_output
						return {"embeddings": embeddings.detach().cpu().numpy()}

			lowercase    :			Dict  =       Features(
			    {"""text""": Value("""string"""    ), """title""": Value("""string"""    ), """embeddings""": Sequence(Value("""float32"""    )    )}    )  # optional, save as float32 instead of float64 to save space

			lowercase    :			Any  =       data_shrad.map(
			    partial(__A ,ctx_encoder=__A ,ctx_tokenizer=__A ,device=__A    ) ,batched=__A ,batch_size=16 ,features=__A ,)
			dataset.save_to_disk(__A    )







def 			__snake_case						(      __A ,__A    )		->				Optional[Any]:
			lowercase    :			str  =       []

			for shard_address in glob(str(__A    ) + """/*/"""    ):
						data_shard_list.append(load_from_disk(__A    )    )

			lowercase    :			str  =       concatenate_datasets(__A    )
			faiss.omp_set_num_threads(96    )

			lowercase    :			Any  =       faiss.IndexHNSWFlat(768 ,128 ,faiss.METRIC_INNER_PRODUCT    )
			concat.add_faiss_index("""embeddings""" ,custom_index=__A    )
			concat.get_index("""embeddings"""    ).save(
			    __A    )  # since we load the index in to memory,we can directly update the index in the disk









"""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


