






"""simple docstring"""



def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int    ):





  '''simple docstring'''





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

  lowercase							    =       0
  lowercase							    =       str(SCREAMING_SNAKE_CASE_    )

  while len(SCREAMING_SNAKE_CASE_    ) != 1:
    lowercase							    =       [int(SCREAMING_SNAKE_CASE_    ) for i in num_string]

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

    lowercase							    =       str(SCREAMING_SNAKE_CASE_    )

    steps += 1
  return steps
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int    ):





  '''simple docstring'''





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

  lowercase							    =       0
  lowercase							    =       str(SCREAMING_SNAKE_CASE_    )

  while len(SCREAMING_SNAKE_CASE_    ) != 1:
    lowercase							    =       [int(SCREAMING_SNAKE_CASE_    ) for i in num_string]

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

    lowercase							    =       str(SCREAMING_SNAKE_CASE_    )

    steps += 1
  return steps


if __name__ == "__main__":
    import doctest

    doctest.testmod()





import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


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

snake_case_			  = ['''names''', '''prefix''']
snake_case_			  = ['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']
snake_case_			  = ['''encoding_errors''', '''on_bad_lines''']
snake_case_			  = ['''date_format''']


@dataclass
class       SCREAMING_SNAKE_CASE__					(datasets.BuilderConfig     ):
    __lowerCamelCase						:       str														=     ","
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       Optional[Union[int, List[int], str]]														=     "infer"
    __lowerCamelCase						:       Optional[List[str]]														=     None
    __lowerCamelCase						:       Optional[List[str]]														=     None
    __lowerCamelCase						:       Optional[Union[int, str, List[int], List[str]]]														=     None
    __lowerCamelCase						:       Optional[Union[List[int], List[str]]]														=     None
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       Optional[Literal["c", "python", "pyarrow"]]														=     None
    __lowerCamelCase						:       Dict[Union[int, str], Callable[[Any], Any]]														=     None
    __lowerCamelCase						:       Optional[list]														=     None
    __lowerCamelCase						:       Optional[list]														=     None
    __lowerCamelCase						:       bool														=     False
    __lowerCamelCase						:       Optional[Union[int, List[int]]]														=     None
    __lowerCamelCase						:       Optional[int]														=     None
    __lowerCamelCase						:       Optional[Union[str, List[str]]]														=     None
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       bool														=     False
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       str														=     "."
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       str														=     '"'
    __lowerCamelCase						:       int														=     0
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       int														=     0
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       bool														=     False
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       int														=     1_0000
    __lowerCamelCase						:       Optional[datasets.Features]														=     None
    __lowerCamelCase						:       Optional[str]														=     "strict"
    __lowerCamelCase						:       Literal["error", "warn", "skip"]														=     "error"
    __lowerCamelCase						:       Optional[str]														=     None





    def       snake_case_				(      self):
         if self.delimiter is not None:
              lowercase__    :   List[Any]      =       self.delimiter
         if self.column_names is not None:
              lowercase__    :   Optional[int]      =       self.column_names





    @property
    def       snake_case_				(      self):
         lowercase__    :   Dict      =       {
             'sep': self.sep,
             'header': self.header,
             'names': self.names,
             'index_col': self.index_col,
             'usecols': self.usecols,
             'prefix': self.prefix,
             'mangle_dupe_cols': self.mangle_dupe_cols,
             'engine': self.engine,
             'converters': self.converters,
             'true_values': self.true_values,
             'false_values': self.false_values,
             'skipinitialspace': self.skipinitialspace,
             'skiprows': self.skiprows,
             'nrows': self.nrows,
             'na_values': self.na_values,
             'keep_default_na': self.keep_default_na,
             'na_filter': self.na_filter,
             'verbose': self.verbose,
             'skip_blank_lines': self.skip_blank_lines,
             'thousands': self.thousands,
             'decimal': self.decimal,
             'lineterminator': self.lineterminator,
             'quotechar': self.quotechar,
             'quoting': self.quoting,
             'escapechar': self.escapechar,
             'comment': self.comment,
             'encoding': self.encoding,
             'dialect': self.dialect,
             'error_bad_lines': self.error_bad_lines,
             'warn_bad_lines': self.warn_bad_lines,
             'skipfooter': self.skipfooter,
             'doublequote': self.doublequote,
             'memory_map': self.memory_map,
             'float_precision': self.float_precision,
             'chunksize': self.chunksize,
             'encoding_errors': self.encoding_errors,
             'on_bad_lines': self.on_bad_lines,
             'date_format': self.date_format,
         }

         # some kwargs must not be passed if they don't have a default value
         # some others are deprecated and we can also not pass them if they are the default value
         for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
              if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()						,   a):
                   del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
         if not (datasets.config.PANDAS_VERSION.major >= 2):
              for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
                   del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
         if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
              for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
                   del pd_read_csv_kwargs[pd_read_csv_parameter]

         return pd_read_csv_kwargs



class       SCREAMING_SNAKE_CASE__					(datasets.ArrowBasedBuilder     ):
    __lowerCamelCase						:       Optional[Any]														=     CsvConfig





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





    def       snake_case_				(      self						,   a):
         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}""")
         lowercase__    :   Any      =       dl_manager.download_and_extract(self.config.data_files)
         if isinstance(a						,   (str, list, tuple)):
              lowercase__    :   List[str]      =       data_files
              if isinstance(a						,   a):
                   lowercase__    :   Optional[Any]      =       [files]
              lowercase__    :   Optional[int]      =       [dl_manager.iter_files(a) for file in files]
              return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,   gen_kwargs={'files': files})]
         lowercase__    :   int      =       []
         for split_name, files in data_files.items():
              if isinstance(a						,   a):
                   lowercase__    :   Optional[int]      =       [files]
              lowercase__    :   Tuple      =       [dl_manager.iter_files(a) for file in files]
              splits.append(datasets.SplitGenerator(name=a						,   gen_kwargs={'files': files}))
         return splits





    def       snake_case_				(      self						,   a):
         if self.config.features is not None:
              lowercase__    :   Optional[int]      =       self.config.features.arrow_schema
              if all(not require_storage_cast(a) for feature in self.config.features.values()):
                   # cheaper cast
                   lowercase__    :   Dict      =       pa.Table.from_arrays([pa_table[field.name] for field in schema]						,   schema=a)
              else:
                   # more expensive cast; allows str <-> int/float or str to Audio for example
                   lowercase__    :   Optional[Any]      =       table_cast(a						,   a)
         return pa_table





    def       snake_case_				(      self						,   a):
         lowercase__    :   List[Any]      =       self.config.features.arrow_schema if self.config.features else None
         # dtype allows reading an int column as str
         lowercase__    :   Optional[int]      =       (
             {
                 name: dtype.to_pandas_dtype() if not require_storage_cast(a) else object
                 for name, dtype, feature in zip(schema.names						,   schema.types						,   self.config.features.values())
             }
             if schema is not None
             else None
         )
         for file_idx, file in enumerate(itertools.chain.from_iterable(a)):
              lowercase__    :   int      =       pd.read_csv(a						,   iterator=a						,   dtype=a						,   **self.config.pd_read_csv_kwargs)
              try:
                   for batch_idx, df in enumerate(a):
                        lowercase__    :   List[str]      =       pa.Table.from_pandas(a)
                        # 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(a)
              except ValueError as e:
                   logger.error(f"""Failed to read file '{file}' with error {type(a)}: {e}""")
                   raise

