





import random
def   lowerCAmelCase_				( __a			,					__a			,					__a = False	)  ->	int:







  """simple docstring"""





  lowerCamelCase__:   dict    						={i: [] for i in range(_lowerCamelCase	)}

  # if probability is greater or equal than 1, then generate a complete graph
  if probability >= 1:
    return complete_graph(_lowerCamelCase	)
  # if probability is lower or equal than 0, then return a graph without edges
  if probability <= 0:
    return graph

  # for each couple of nodes, add an edge from u to v
  # if the number randomly generated is greater than probability probability
  for i in range(_lowerCamelCase	):
    for j in range(i + 1			,					_lowerCamelCase	):
      if random.random() < probability:
        graph[i].append(_lowerCamelCase	)
        if not directed:
          # if the graph is undirected, add an edge in from j to i, either
          graph[j].append(_lowerCamelCase	)
  return graph
def   lowerCAmelCase_				( __a	)  ->	Optional[Any]:







  """simple docstring"""





  return {
      i: [j for j in range(_lowerCamelCase	) if i != j] for i in range(_lowerCamelCase	)
  }


if __name__ == "__main__":
     import doctest

     doctest.testmod()








import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


__A             =       datasets.utils.logging.get_logger(__name__)



@dataclass
class      _SCREAMING_SNAKE_CASE		(			datasets.BuilderConfig					):







 '''simple docstring'''
 lowercase_	=    1_0000
 lowercase_	=    None
 lowercase_	=    None



class      _SCREAMING_SNAKE_CASE		(			datasets.ArrowBasedBuilder					):







 '''simple docstring'''
 lowercase_	=    ParquetConfig

 def 							SCREAMING_SNAKE_CASE_      (self       :     List[Any]) ->Any:



   '''simple docstring'''



   return datasets.DatasetInfo(features=self.config.features)

 def 							SCREAMING_SNAKE_CASE_      (self       :     Any ,				UpperCAmelCase_       :     Any) ->Any:



   '''simple docstring'''



   if not self.config.data_files:
     raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""")
   lowerCamelCase__:   Optional[Any]    						=dl_manager.download_and_extract(self.config.data_files)
   if isinstance(UpperCAmelCase_ ,				(str, list, tuple)):
     lowerCamelCase__:   Any    						=data_files
     if isinstance(UpperCAmelCase_ ,				UpperCAmelCase_):
       lowerCamelCase__:   Union[str, Any]    						=[files]
     # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
     lowerCamelCase__:   Optional[int]    						=[dl_manager.iter_files(UpperCAmelCase_) for file in files]
     return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,				gen_kwargs={"files": files})]
   lowerCamelCase__:   int    						=[]
   for split_name, files in data_files.items():
     if isinstance(UpperCAmelCase_ ,				UpperCAmelCase_):
       lowerCamelCase__:   List[Any]    						=[files]
     # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
     lowerCamelCase__:   str    						=[dl_manager.iter_files(UpperCAmelCase_) for file in files]
     # Infer features is they are stoed in the arrow schema
     if self.info.features is None:
       for file in itertools.chain.from_iterable(UpperCAmelCase_):
         with open(UpperCAmelCase_ ,				"rb") as f:
           lowerCamelCase__:   Union[str, Any]    						=datasets.Features.from_arrow_schema(pq.read_schema(UpperCAmelCase_))
         break
     splits.append(datasets.SplitGenerator(name=UpperCAmelCase_ ,				gen_kwargs={"files": files}))
   return splits

 def 							SCREAMING_SNAKE_CASE_      (self       :     Any ,				UpperCAmelCase_       :     pa.Table) ->pa.Table:



   '''simple docstring'''



   if self.info.features is not None:
     # more expensive cast to support nested features with keys in a different order
     # allows str <-> int/float or str to Audio for example
     lowerCamelCase__:   str    						=table_cast(UpperCAmelCase_ ,				self.info.features.arrow_schema)
   return pa_table







 def 							SCREAMING_SNAKE_CASE_      (self       :     str ,				UpperCAmelCase_       :     Optional[Any]) ->int:



   '''simple docstring'''



   lowerCamelCase__:   str    						=self.info.features.arrow_schema if self.info.features is not None else None
   if self.info.features is not None and self.config.columns is not None:
     if sorted(field.name for field in schema) != sorted(self.config.columns):
       raise ValueError(
           F"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'""")
   for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase_)):
     with open(UpperCAmelCase_ ,				"rb") as f:
       lowerCamelCase__:   Optional[Any]    						=pq.ParquetFile(UpperCAmelCase_)
       try:
         for batch_idx, record_batch in enumerate(
             parquet_file.iter_batches(batch_size=self.config.batch_size ,				columns=self.config.columns)):
           lowerCamelCase__:   Optional[Any]    						=pa.Table.from_batches([record_batch])
           # Uncomment for debugging (will print the Arrow table size and elements)
           # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
           # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
           yield F"""{file_idx}_{batch_idx}""", self._cast_table(UpperCAmelCase_)
       except ValueError as e:
         logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase_)}: {e}""")
         raise

