



"""simple docstring"""







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


lowercase__		:    int				   =     datasets.utils.logging.get_logger(__name__)







@dataclass
class 				UpperCamelCase__       (     datasets.BuilderConfig  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					1_0000
      _SCREAMING_SNAKE_CASE      =					None
      _SCREAMING_SNAKE_CASE      =					None







class 				UpperCamelCase__       (     datasets.ArrowBasedBuilder  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					ParquetConfig



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict	):
        return datasets.DatasetInfo(features=self.config.features	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				List[str]	):
        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_       :    List[str]								=			dl_manager.download_and_extract(self.config.data_files	)
        if isinstance(SCREAMING_SNAKE_CASE_   ,      (str, list, tuple)	):
          lowerCAmelCase_       :    Optional[int]								=			data_files
          if isinstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	):
            lowerCAmelCase_       :    Dict								=			[files]
          # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
          lowerCAmelCase_       :    List[str]								=			[dl_manager.iter_files(SCREAMING_SNAKE_CASE_	) for file in files]
          return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,      gen_kwargs={'files': files}	)]
        lowerCAmelCase_       :    Optional[int]								=			[]
        for split_name, files in data_files.items():
          if isinstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	):
            lowerCAmelCase_       :    Any								=			[files]
          # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
          lowerCAmelCase_       :    Tuple								=			[dl_manager.iter_files(SCREAMING_SNAKE_CASE_	) 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(SCREAMING_SNAKE_CASE_	):
              with open(SCREAMING_SNAKE_CASE_   ,      'rb'	) as f:
                lowerCAmelCase_       :    List[str]								=			datasets.Features.from_arrow_schema(pq.read_schema(SCREAMING_SNAKE_CASE_	)	)
              break
          splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE_   ,      gen_kwargs={'files': files}	)	)
        return splits



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				pa.Table	):
        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_       :    int								=			table_cast(SCREAMING_SNAKE_CASE_   ,      self.info.features.arrow_schema	)
        return pa_table





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]	):
        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(SCREAMING_SNAKE_CASE_	)	):
          with open(SCREAMING_SNAKE_CASE_   ,      'rb'	) as f:
            lowerCAmelCase_       :    Optional[Any]								=			pq.ParquetFile(SCREAMING_SNAKE_CASE_	)
            try:
              for batch_idx, record_batch in enumerate(
                  parquet_file.iter_batches(batch_size=self.config.batch_size   ,      columns=self.config.columns	)	):
                lowerCAmelCase_       :    Optional[int]								=			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(SCREAMING_SNAKE_CASE_	)
            except ValueError as e:
              logger.error(F"Failed to read file '{file}' with error {type(SCREAMING_SNAKE_CASE_	)}: {e}"	)
              raise










"""simple docstring"""







def     UpperCamelCase_   (   lowerCAmelCase__				:    Optional[Any]   ,       lowerCAmelCase__				:    str						)						->       List[Any]:

  """simple docstring"""




  return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2

def     UpperCamelCase_   (   lowerCAmelCase__				:    List[Any]   ,       lowerCAmelCase__				:    str=0						)						->       Union[str, Any]:

  """simple docstring"""




  return sorted(lowerCAmelCase__   ,       key=lambda lowerCAmelCase__						: x[column]						)

def     UpperCamelCase_   (   lowerCAmelCase__				:    Dict   ,       lowerCAmelCase__				:    Optional[int]   ,       lowerCAmelCase__				:    Optional[Any]=float('inf'						)						)						->       Optional[int]:

  """simple docstring"""




  for i in range(points_counts - 1						):
    for j in range(i + 1   ,       lowerCAmelCase__						):
      lowerCAmelCase_       :    Union[str, Any]								=			euclidean_distance_sqr(points[i]   ,       points[j]						)
      if current_dis < min_dis:
        lowerCAmelCase_       :    Optional[int]								=			current_dis
  return min_dis

def     UpperCamelCase_   (   lowerCAmelCase__				:    List[Any]   ,       lowerCAmelCase__				:    Dict   ,       lowerCAmelCase__				:    Union[str, Any]=float('inf'						)						)						->       Dict:

  """simple docstring"""




  for i in range(min(6   ,       points_counts - 1						)   ,       lowerCAmelCase__						):
    for j in range(max(0   ,       i - 6						)   ,       lowerCAmelCase__						):
      lowerCAmelCase_       :    Any								=			euclidean_distance_sqr(points[i]   ,       points[j]						)
      if current_dis < min_dis:
        lowerCAmelCase_       :    Union[str, Any]								=			current_dis
  return min_dis

def     UpperCamelCase_   (   lowerCAmelCase__				:    Dict   ,       lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    Optional[int]						)						->       Dict:

  """simple docstring"""




  if points_counts <= 3:
    return dis_between_closest_pair(lowerCAmelCase__   ,       lowerCAmelCase__						)

  # recursion
  lowerCAmelCase_       :    int								=			points_counts // 2
  lowerCAmelCase_       :    Optional[Any]								=			closest_pair_of_points_sqr(
      lowerCAmelCase__   ,       points_sorted_on_y[:mid]   ,       lowerCAmelCase__						)
  lowerCAmelCase_       :    Optional[Any]								=			closest_pair_of_points_sqr(
      lowerCAmelCase__   ,       points_sorted_on_y[mid:]   ,       points_counts - mid						)
  lowerCAmelCase_       :    Any								=			min(lowerCAmelCase__   ,       lowerCAmelCase__						)
  lowerCAmelCase_       :    str								=			[]
  for point in points_sorted_on_x:
    if abs(point[0] - points_sorted_on_x[mid][0]						) < closest_pair_dis:
      cross_strip.append(lowerCAmelCase__						)

  lowerCAmelCase_       :    List[Any]								=			dis_between_closest_in_strip(
      lowerCAmelCase__   ,       len(lowerCAmelCase__						)   ,       lowerCAmelCase__						)
  return min(lowerCAmelCase__   ,       lowerCAmelCase__						)

def     UpperCamelCase_   (   lowerCAmelCase__				:    str   ,       lowerCAmelCase__				:    Tuple						)						->       List[Any]:

  """simple docstring"""




  lowerCAmelCase_       :    List[str]								=			column_based_sort(lowerCAmelCase__   ,       column=0						)
  lowerCAmelCase_       :    Dict								=			column_based_sort(lowerCAmelCase__   ,       column=1						)
  return (
      closest_pair_of_points_sqr(
          lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)
  ) ** 0.5


if __name__ == "__main__":
     lowercase__		:    List[str]				   =     [(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
     print("""Distance:""", closest_pair_of_points(points, len(points)))





