






def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						int:

       if not isinstance(__snake_case   ,      __snake_case ):
              raise ValueError("""multiplicative_persistence() only accepts integral values""" )
       if num < 0:
              raise ValueError("""multiplicative_persistence() does not accept negative values""" )

       _UpperCAmelCase							=    0
       _UpperCAmelCase							=    str(__snake_case )

       while len(__snake_case ) != 1:
              _UpperCAmelCase							=    [int(__snake_case ) for i in num_string]

              _UpperCAmelCase							=    1
              for i in range(0   ,      len(__snake_case ) ):
                     total *= numbers[i]

              _UpperCAmelCase							=    str(__snake_case )

              steps += 1
       return steps




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						int:

       if not isinstance(__snake_case   ,      __snake_case ):
              raise ValueError("""additive_persistence() only accepts integral values""" )
       if num < 0:
              raise ValueError("""additive_persistence() does not accept negative values""" )

       _UpperCAmelCase							=    0
       _UpperCAmelCase							=    str(__snake_case )

       while len(__snake_case ) != 1:
              _UpperCAmelCase							=    [int(__snake_case ) for i in num_string]

              _UpperCAmelCase							=    0
              for i in range(0   ,      len(__snake_case ) ):
                     total += numbers[i]

              _UpperCAmelCase							=    str(__snake_case )

              steps += 1
       return steps


if __name__ == "__main__":
  import doctest

  doctest.testmod()







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


__a:     Tuple   					=					datasets.utils.logging.get_logger(__name__)
@dataclass
class 		SCREAMING_SNAKE_CASE__	(  datasets.BuilderConfig     ):

  '''simple docstring'''







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



class 		SCREAMING_SNAKE_CASE__	(  datasets.ArrowBasedBuilder     ):

  '''simple docstring'''







  _lowerCamelCase    =  JsonConfig


  def  lowerCamelCase       (      self		:			Any							)						->	Any:


         """simple docstring"""
         if self.config.block_size is not None:
                logger.warning("""The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"""							)
                _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			Optional[int]							)						->	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}"""							)
         _UpperCAmelCase							=    dl_manager.download_and_extract(self.config.data_files							)
         if isinstance(lowerCamelCase       ,				(str, list, tuple)							):
                _UpperCAmelCase							=    data_files
                if isinstance(lowerCamelCase       ,				lowerCamelCase							):
                       _UpperCAmelCase							=    [files]
                _UpperCAmelCase							=    [dl_manager.iter_files(lowerCamelCase							) for file in files]
                return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,				gen_kwargs={"""files""": files}							)]
         _UpperCAmelCase							=    []
         for split_name, files in data_files.items():
                if isinstance(lowerCamelCase       ,				lowerCamelCase							):
                       _UpperCAmelCase							=    [files]
                _UpperCAmelCase							=    [dl_manager.iter_files(lowerCamelCase							) for file in files]
                splits.append(datasets.SplitGenerator(name=lowerCamelCase       ,				gen_kwargs={"""files""": files}							)							)
         return splits


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			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							):
                       _UpperCAmelCase							=    self.config.features.arrow_schema.field(lowerCamelCase							).type
                       _UpperCAmelCase							=    pa_table.append_column(lowerCamelCase       ,				pa.array([None] * len(lowerCamelCase							)       ,				type=lowerCamelCase							)							)
                # more expensive cast to support nested structures with keys in a different order
                # allows str <-> int/float or str to Audio for example
                _UpperCAmelCase							=    table_cast(lowerCamelCase       ,				self.config.features.arrow_schema							)
         return pa_table





  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			Optional[int]							)						->	str:


         """simple docstring"""
         for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCamelCase							)							):
                # 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(lowerCamelCase       ,				encoding=self.config.encoding       ,				errors=self.config.encoding_errors							) as f:
                              _UpperCAmelCase							=    json.load(lowerCamelCase							)

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

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

                # If the file has one json object per line
                else:
                       with open(lowerCamelCase       ,				"""rb"""							) as f:
                              _UpperCAmelCase							=    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
                              _UpperCAmelCase							=    max(self.config.chunksize // 32       ,				16 << 10							)
                              _UpperCAmelCase							=    (
                                  self.config.encoding_errors if self.config.encoding_errors is not None else """strict"""
                              )
                              while True:
                                     _UpperCAmelCase							=    f.read(self.config.chunksize							)
                                     if not batch:
                                            break
                                     # Finish current line
                                     try:
                                            batch += f.readline()
                                     except (AttributeError, io.UnsupportedOperation):
                                            batch += readline(lowerCamelCase							)
                                     # PyArrow only accepts utf-8 encoded bytes
                                     if self.config.encoding != "utf-8":
                                            _UpperCAmelCase							=    batch.decode(self.config.encoding       ,				errors=lowerCamelCase							).encode("""utf-8"""							)
                                     try:
                                            while True:
                                                   try:
                                                          _UpperCAmelCase							=    paj.read_json(
                                                              io.BytesIO(lowerCamelCase							)       ,				read_options=paj.ReadOptions(block_size=lowerCamelCase							)							)
                                                          break
                                                   except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                                          if (
                                                              isinstance(lowerCamelCase       ,				pa.ArrowInvalid							)
                                                              and "straddling" not in str(lowerCamelCase							)
                                                              or block_size > len(lowerCamelCase							)
                                                          ):
                                                                 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(lowerCamelCase							)} 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(
                                                       lowerCamelCase       ,				encoding=self.config.encoding       ,				errors=self.config.encoding_errors							) as f:
                                                          _UpperCAmelCase							=    json.load(lowerCamelCase							)
                                            except json.JSONDecodeError:
                                                   logger.error(f"""Failed to read file '{file}' with error {type(lowerCamelCase							)}: {e}"""							)
                                                   raise e
                                            # If possible, parse the file as a list of json objects and exit the loop
                                            if isinstance(lowerCamelCase       ,				lowerCamelCase							):  # list is the only sequence type supported in JSON
                                                   try:
                                                          _UpperCAmelCase							=    set().union(*[row.keys() for row in dataset]							)
                                                          _UpperCAmelCase							=    {col: [row.get(lowerCamelCase							) for row in dataset] for col in keys}
                                                          _UpperCAmelCase							=    pa.Table.from_pydict(lowerCamelCase							)
                                                   except (pa.ArrowInvalid, AttributeError) as e:
                                                          logger.error(f"""Failed to read file '{file}' with error {type(lowerCamelCase							)}: {e}"""							)
                                                          raise ValueError(f"""Not able to read records in the JSON file at {file}."""							) from None
                                                   yield file_idx, self._cast_table(lowerCamelCase							)
                                                   break
                                            else:
                                                   logger.error(f"""Failed to read file '{file}' with error {type(lowerCamelCase							)}: {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(lowerCamelCase							)
                                     batch_idx += 1