




import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


_lowerCamelCase	:		Optional[Any]							       =   datasets.utils.logging.get_logger(__name__)


@dataclass
class 				UpperCamelCase_    ( datasets.BuilderConfig     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      None
    UpperCAmelCase__							  =      "utf-8"
    UpperCAmelCase__							  =      None
    UpperCAmelCase__							  =      None
    UpperCAmelCase__							  =      True  # deprecated
    UpperCAmelCase__							  =      None  # deprecated
    UpperCAmelCase__							  =      10 << 20  # 10MB
    UpperCAmelCase__							  =      None


class 				UpperCamelCase_    ( datasets.ArrowBasedBuilder     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      JsonConfig


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->str:





           '''simple docstring'''

           if self.config.block_size is not None:
                  logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead''')
                  A__       =    self.config.block_size
           if self.config.use_threads is not True:
                  logger.warning(
                      '''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.''')
           if self.config.newlines_in_values is not None:
                  raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported''')
           return datasets.DatasetInfo(features=self.config.features)


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	List[Any])						->Dict:





           '''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}""")
           A__       =    dl_manager.download_and_extract(self.config.data_files)
           if isinstance(UpperCAmelCase__					, (str, list, tuple)):
                  A__       =    data_files
                  if isinstance(UpperCAmelCase__					, UpperCAmelCase__):
                         A__       =    [files]
                  A__       =    [dl_manager.iter_files(UpperCAmelCase__) for file in files]
                  return [datasets.SplitGenerator(name=datasets.Split.TRAIN					, gen_kwargs={'''files''': files})]
           A__       =    []
           for split_name, files in data_files.items():
                  if isinstance(UpperCAmelCase__					, UpperCAmelCase__):
                         A__       =    [files]
                  A__       =    [dl_manager.iter_files(UpperCAmelCase__) for file in files]
                  splits.append(datasets.SplitGenerator(name=UpperCAmelCase__					, gen_kwargs={'''files''': files}))
           return splits


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	pa.Table)						->pa.Table:





           '''simple docstring'''

           if self.config.features is not None:
                  # adding missing columns
                  for column_name in set(self.config.features) - set(pa_table.column_names):
                         A__       =    self.config.features.arrow_schema.field(UpperCAmelCase__).type
                         A__       =    pa_table.append_column(UpperCAmelCase__					, pa.array([None] * len(UpperCAmelCase__)					, type=UpperCAmelCase__))
                  # more expensive cast to support nested structures with keys in a different order
                  # allows str <-> int/float or str to Audio for example
                  A__       =    table_cast(UpperCAmelCase__					, self.config.features.arrow_schema)
           return pa_table





    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any]					, UpperCAmelCase__      :	Tuple)						->str:





           '''simple docstring'''

           for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase__)):
                  # If the file is one json object and if we need to look at the list of items in one specific field
                  if self.config.field is not None:
                         with open(UpperCAmelCase__					, encoding=self.config.encoding					, errors=self.config.encoding_errors) as f:
                                A__       =    json.load(UpperCAmelCase__)

                         # We keep only the field we are interested in
                         A__       =    dataset[self.config.field]

                         # We accept two format: a list of dicts or a dict of lists
                         if isinstance(UpperCAmelCase__					, (list, tuple)):
                                A__       =    set().union(*[row.keys() for row in dataset])
                                A__       =    {col: [row.get(UpperCAmelCase__) for row in dataset] for col in keys}
                         else:
                                A__       =    dataset
                         A__       =    pa.Table.from_pydict(UpperCAmelCase__)
                         yield file_idx, self._cast_table(UpperCAmelCase__)

                  # If the file has one json object per line
                  else:
                         with open(UpperCAmelCase__					, '''rb''') as f:
                                A__       =    0
                                # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                                # Set a default minimum value of 16kB if the chunk size is really small
                                A__       =    max(self.config.chunksize // 32					, 16 << 10)
                                A__       =    (
                                    self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
                                )
                                while True:
                                       A__       =    f.read(self.config.chunksize)
                                       if not batch:
                                              break
                                       # Finish current line
                                       try:
                                              batch += f.readline()
                                       except (AttributeError, io.UnsupportedOperation):
                                              batch += readline(UpperCAmelCase__)
                                       # PyArrow only accepts utf-8 encoded bytes
                                       if self.config.encoding != "utf-8":
                                              A__       =    batch.decode(self.config.encoding					, errors=UpperCAmelCase__).encode('''utf-8''')
                                       try:
                                              while True:
                                                     try:
                                                            A__       =    paj.read_json(
                                                                io.BytesIO(UpperCAmelCase__)					, read_options=paj.ReadOptions(block_size=UpperCAmelCase__))
                                                            break
                                                     except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                                            if (
                                                                isinstance(UpperCAmelCase__					, pa.ArrowInvalid)
                                                                and "straddling" not in str(UpperCAmelCase__)
                                                                or block_size > len(UpperCAmelCase__)
                                                            ):
                                                                   raise
                                                            else:
                                                                   # Increase the block size in case it was too small.
                                                                   # The block size will be reset for the next file.
                                                                   logger.debug(
                                                                       f"""Batch of {len(UpperCAmelCase__)} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.""")
                                                                   block_size *= 2
                                       except pa.ArrowInvalid as e:
                                              try:
                                                     with open(
                                                         UpperCAmelCase__					, encoding=self.config.encoding					, errors=self.config.encoding_errors) as f:
                                                            A__       =    json.load(UpperCAmelCase__)
                                              except json.JSONDecodeError:
                                                     logger.error(f"""Failed to read file '{file}' with error {type(UpperCAmelCase__)}: {e}""")
                                                     raise e
                                              # If possible, parse the file as a list of json objects and exit the loop
                                              if isinstance(UpperCAmelCase__					, UpperCAmelCase__):  # list is the only sequence type supported in JSON
                                                     try:
                                                            A__       =    set().union(*[row.keys() for row in dataset])
                                                            A__       =    {col: [row.get(UpperCAmelCase__) for row in dataset] for col in keys}
                                                            A__       =    pa.Table.from_pydict(UpperCAmelCase__)
                                                     except (pa.ArrowInvalid, AttributeError) as e:
                                                            logger.error(f"""Failed to read file '{file}' with error {type(UpperCAmelCase__)}: {e}""")
                                                            raise ValueError(f"""Not able to read records in the JSON file at {file}.""") from None
                                                     yield file_idx, self._cast_table(UpperCAmelCase__)
                                                     break
                                              else:
                                                     logger.error(f"""Failed to read file '{file}' with error {type(UpperCAmelCase__)}: {e}""")
                                                     raise ValueError(
                                                         f"""Not able to read records in the JSON file at {file}. """
                                                         f"""You should probably indicate the field of the JSON file containing your records. """
                                                         f"""This JSON file contain the following fields: {str(list(dataset.keys()))}. """
                                                         f"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """) from None
                        # 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 (file_idx, batch_idx), self._cast_table(UpperCAmelCase__)
                                       batch_idx += 1








from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

_lowerCamelCase	:		str							       =   typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
_lowerCamelCase	:		Tuple							       =   typing.Union[np.floataa, int, float]  # noqa: UP007







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       VectorOut:



       """simple docstring"""

       return np.sqrt(np.sum((np.asarray(lowercase_						) - np.asarray(lowercase_						)) ** 2						)						)







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       VectorOut:



       """simple docstring"""

       return sum((va - va) ** 2 for va, va in zip(lowercase_ ,				lowercase_						)						) ** (1 / 2)


if __name__ == "__main__":







  def SCREAMING_SNAKE_CASE				(       )   ->       None:



         """simple docstring"""

         from timeit import timeit

         print('''Without Numpy'''						)
         print(
             timeit(
                 '''euclidean_distance_no_np([1, 2, 3], [4, 5, 6])''' ,				number=10_000 ,				globals=globals() ,				)						)
         print('''With Numpy'''						)
         print(
             timeit(
                 '''euclidean_distance([1, 2, 3], [4, 5, 6])''' ,				number=10_000 ,				globals=globals() ,				)						)
  benchmark()


