
'''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  :				Any			,       _a  :				int = 512      ):
      snake_case_    							=				x.shape[1] if len(x.shape      ) > 1 else 1
      snake_case_    							=				1 + np.arange(0			,       feature_num * offset			,       _a			,       dtype=np.intaa      )
      snake_case_    							=				x + feature_offset
      return x
def 	snake_case_							(				_a  :				Any			,       _a  :				List[str]=True      ):
      requires_backends(_a			,       ["cython"]      )

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

      snake_case_    							=				np.asarray(item["edge_index"]			,       dtype=np.intaa      )

      snake_case_    							=				convert_to_single_emb(_a      ) + 1
      snake_case_    							=				item["num_nodes"]

      if len(edge_attr.shape      ) == 1:
            snake_case_    							=				edge_attr[:, None]
      snake_case_    							=				np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]			,       dtype=np.intaa      )
      snake_case_    							=				convert_to_single_emb(_a      ) + 1

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

      snake_case_							,						snake_case_    							=				algos_graphormer.floyd_warshall(_a      )
      snake_case_    							=				np.amax(_a      )

      snake_case_    							=				algos_graphormer.gen_edge_input(_a			,       _a			,       _a      )
      snake_case_    							=				np.zeros([num_nodes + 1, num_nodes + 1]			,       dtype=np.single      )  # with graph token

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

      return item




class 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			int						,			lowerCAmelCase_	:			Optional[int]=20						,			lowerCAmelCase_	:			Any=False     ) ->							Tuple:
        if not is_cython_available():
              raise ImportError("Graphormer preprocessing needs Cython (pyximport)"     )

        snake_case_    							=				spatial_pos_max
        snake_case_    							=				on_the_fly_processing


  def __call__(self	:			Union[str, Any]						,			lowerCAmelCase_	:			List[dict]     ) ->							Dict[str, Any]:
        if self.on_the_fly_processing:
              snake_case_    							=				[preprocess_item(lowerCAmelCase_     ) for i in features]

        if not isinstance(features[0]						,			lowerCAmelCase_     ):
              snake_case_    							=				[vars(lowerCAmelCase_     ) for f in features]
        snake_case_    							=				{}

        snake_case_    							=				max(len(i["input_nodes"]     ) for i in features     )
        snake_case_    							=				len(features[0]["input_nodes"][0]     )
        snake_case_    							=				len(features[0]["attn_edge_type"][0][0]     )
        snake_case_    							=				max(len(i["input_edges"][0][0]     ) for i in features     )
        snake_case_    							=				len(features[0]["input_edges"][0][0][0]     )
        snake_case_    							=				len(lowerCAmelCase_     )

        snake_case_    							=				torch.zeros(lowerCAmelCase_						,			max_node_num + 1						,			max_node_num + 1						,			dtype=torch.float     )
        snake_case_    							=				torch.zeros(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			dtype=torch.long     )
        snake_case_    							=				torch.zeros(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			dtype=torch.long     )
        snake_case_    							=				torch.zeros(lowerCAmelCase_						,			lowerCAmelCase_						,			dtype=torch.long     )
        snake_case_    							=				torch.zeros(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			dtype=torch.long     )
        snake_case_    							=				torch.zeros(
            lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			dtype=torch.long     )

        for ix, f in enumerate(lowerCAmelCase_     ):
              for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
                    snake_case_    							=				torch.tensor(f[k]     )

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

              snake_case_    							=				f["attn_bias"]
              snake_case_    							=				f[
                  "attn_edge_type"
              ]
              snake_case_    							=				f["spatial_pos"]
              snake_case_    							=				f["in_degree"]
              snake_case_    							=				f["input_nodes"]
              snake_case_    							=				f["input_edges"]

        snake_case_    							=				batch["in_degree"]

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

        return batch






import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


UpperCAmelCase :				Any							=1000_0000_0000
UpperCAmelCase :				Any							=1_0000

UpperCAmelCase			,			UpperCAmelCase :				Tuple							=os.path.split(__file__)
UpperCAmelCase :				Tuple							=os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase):
					UpperCamelCase_			= pa.Table.from_pydict({"col": [0] * chunk_size})
					UpperCamelCase_			= pa.concat_tables([table] * (num_examples // chunk_size))
					return datasets.Dataset(_lowerCAmelCase ,						fingerprint="table_100B")


@dataclass
class    _lowercase :
				'''simple docstring'''







				lowercase__     =    42
				lowercase__     =    42
				lowercase__     =    42
				lowercase__     =    42

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= np.random.default_rng(self.seed     )
									UpperCamelCase_			= rng.integers(low=self.low  ,			high=self.high  ,			size=self.size     ).tolist()

				def __iter__(			self     ):

									'''simple docstring'''


									return iter(self._sampled_values     )

				def __len__(			self     ):

									'''simple docstring'''


									return self.size




@get_duration
def  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_			= dataset[0]



@get_duration
def  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_			= dataset[-1]



@get_duration
def  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_			= dataset[range(len(_lowerCAmelCase) // 2 ,						len(_lowerCAmelCase) // 2 + 10_24)]



@get_duration
def  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_			= dataset[RandIter(0 ,						len(_lowerCAmelCase) ,						10_24 ,						seed=42)]



def  _lowerCAmelCase     ():
					UpperCamelCase_			= {"num examples": SPEED_TEST_N_EXAMPLES}
					UpperCamelCase_			= (get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
					print("generating dataset")
					UpperCamelCase_			= generate_100B_dataset(num_examples=_lowerCAmelCase ,						chunk_size=_lowerCAmelCase)
					print("Functions")
					for func in functions:
										print(func.__name__)
										UpperCamelCase_			= func(_lowerCAmelCase)

					with open(_lowerCAmelCase ,						"wb") as f:
										f.write(json.dumps(_lowerCAmelCase).encode("utf-8"))


if __name__ == "__main__":  # useful to run the profiler
	benchmark_table_100B()
