





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


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

@dataclass
class 		SCREAMING_SNAKE_CASE__				(       datasets.BuilderConfig  ):
      A_						:				int							=    10_000
      A_						:				Optional[List[str]]							=    None
      A_						:				Optional[datasets.Features]							=    None






class 		SCREAMING_SNAKE_CASE__				(       datasets.ArrowBasedBuilder  ):
      A_						:				Optional[Any]							=    ParquetConfig


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



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


      def    a (self       :      List[str]				,			a__       :      List[str]      ):





         """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}"""      )
         __snake_case							       =   dl_manager.download_and_extract(self.config.data_files      )
         if isinstance(a__				,			(str, list, tuple)      ):
            __snake_case							       =   data_files
            if isinstance(a__				,			a__      ):
               __snake_case							       =   [files]
            # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
            __snake_case							       =   [dl_manager.iter_files(a__      ) for file in files]
            return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,			gen_kwargs={'''files''': files}      )]
         __snake_case							       =   []
         for split_name, files in data_files.items():
            if isinstance(a__				,			a__      ):
               __snake_case							       =   [files]
            # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
            __snake_case							       =   [dl_manager.iter_files(a__      ) 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(a__      ):
                  with open(a__				,			'''rb'''      ) as f:
                     __snake_case							       =   datasets.Features.from_arrow_schema(pq.read_schema(a__      )      )
                  break
            splits.append(datasets.SplitGenerator(name=a__				,			gen_kwargs={'''files''': files}      )      )
         return splits


      def    a (self       :      str				,			a__       :      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
            __snake_case							       =   table_cast(a__				,			self.info.features.arrow_schema      )
         return pa_table


      def    a (self       :      Any				,			a__       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   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(a__      )      ):
            with open(a__				,			'''rb'''      ) as f:
               __snake_case							       =   pq.ParquetFile(a__      )
               try:
                  for batch_idx, record_batch in enumerate(
                      parquet_file.iter_batches(batch_size=self.config.batch_size				,			columns=self.config.columns      )      ):
                     __snake_case							       =   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(a__      )
               except ValueError as e:
                  logger.error(f"""Failed to read file '{file}' with error {type(a__      )}: {e}"""      )
                  raise









from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

a		:       Dict			     = typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
a		:       Any			     = typing.Union[np.floataa, int, float]  # noqa: UP007






def 				lowerCamelCase__      (    __lowerCamelCase			:     Vector  ,		__lowerCamelCase			:     Vector		):
  return np.sqrt(np.sum((np.asarray(__lowerCamelCase		) - np.asarray(__lowerCamelCase		)) ** 2		)		)






def 				lowerCamelCase__      (    __lowerCamelCase			:     Vector  ,		__lowerCamelCase			:     Vector		):
  return sum((va - va) ** 2 for va, va in zip(__lowerCamelCase  ,		__lowerCamelCase		)		) ** (1 / 2)


if __name__ == "__main__":






       def 				lowerCamelCase__      (    ):
         from timeit import timeit

         print("""Without Numpy"""		)
         print(
             timeit(
                 """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"""  ,		number=10000  ,		globals=globals()  ,		)		)
         print("""With Numpy"""		)
         print(
             timeit(
                 """euclidean_distance([1, 2, 3], [4, 5, 6])"""  ,		number=10000  ,		globals=globals()  ,		)		)







       benchmark()


