'''simple docstring'''

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


_A			:      List[Any]					      =				datasets.utils.logging.get_logger(__name__)

_A			:      Union[str, Any]					      =				['''names''', '''prefix''']
_A			:      Tuple					      =				['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']
_A			:      str					      =				['''encoding_errors''', '''on_bad_lines''']
_A			:      Optional[int]					      =				['''date_format''']







@dataclass
class 				_lowercase     (				datasets.BuilderConfig			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       str      	=					","
 _SCREAMING_SNAKE_CASE     :       Optional[str]      	=					None
 _SCREAMING_SNAKE_CASE     :       Optional[Union[int, List[int], str]]      	=					"infer"
 _SCREAMING_SNAKE_CASE     :       Optional[List[str]]      	=					None
 _SCREAMING_SNAKE_CASE     :       Optional[List[str]]      	=					None
 _SCREAMING_SNAKE_CASE     :       Optional[Union[int, str, List[int], List[str]]]      	=					None
 _SCREAMING_SNAKE_CASE     :       Optional[Union[List[int], List[str]]]      	=					None
 _SCREAMING_SNAKE_CASE     :       Optional[str]      	=					None
 _SCREAMING_SNAKE_CASE     :       bool      	=					True
 _SCREAMING_SNAKE_CASE     :       Optional[Literal["c", "python", "pyarrow"]]      	=					None
 _SCREAMING_SNAKE_CASE     :       Dict[Union[int, str], Callable[[Any], Any]]      	=					None
 _SCREAMING_SNAKE_CASE     :       Optional[list]      	=					None
 _SCREAMING_SNAKE_CASE     :       Optional[list]      	=					None
 _SCREAMING_SNAKE_CASE     :       bool      	=					False
 _SCREAMING_SNAKE_CASE     :       Optional[Union[int, List[int]]]      	=					None
 _SCREAMING_SNAKE_CASE     :       Optional[int]      	=					None
 _SCREAMING_SNAKE_CASE     :       Optional[Union[str, List[str]]]      	=					None
 _SCREAMING_SNAKE_CASE     :       bool      	=					True
 _SCREAMING_SNAKE_CASE     :       bool      	=					True
 _SCREAMING_SNAKE_CASE     :       bool      	=					False
 _SCREAMING_SNAKE_CASE     :       bool      	=					True
 _SCREAMING_SNAKE_CASE     :       Optional[str]      	=					None
 _SCREAMING_SNAKE_CASE     :       str      	=					"."
 _SCREAMING_SNAKE_CASE     :       Optional[str]      	=					None
 _SCREAMING_SNAKE_CASE     :       str      	=					'"'
 _SCREAMING_SNAKE_CASE     :       int      	=					0
 _SCREAMING_SNAKE_CASE     :       Optional[str]      	=					None
 _SCREAMING_SNAKE_CASE     :       Optional[str]      	=					None
 _SCREAMING_SNAKE_CASE     :       Optional[str]      	=					None
 _SCREAMING_SNAKE_CASE     :       Optional[str]      	=					None
 _SCREAMING_SNAKE_CASE     :       bool      	=					True
 _SCREAMING_SNAKE_CASE     :       bool      	=					True
 _SCREAMING_SNAKE_CASE     :       int      	=					0
 _SCREAMING_SNAKE_CASE     :       bool      	=					True
 _SCREAMING_SNAKE_CASE     :       bool      	=					False
 _SCREAMING_SNAKE_CASE     :       Optional[str]      	=					None
 _SCREAMING_SNAKE_CASE     :       int      	=					10000
 _SCREAMING_SNAKE_CASE     :       Optional[datasets.Features]      	=					None
 _SCREAMING_SNAKE_CASE     :       Optional[str]      	=					"strict"
 _SCREAMING_SNAKE_CASE     :       Literal["error", "warn", "skip"]      	=					"error"
 _SCREAMING_SNAKE_CASE     :       Optional[str]      	=					None
 def a					(      self   :	int							)  ->    List[Any]:
  if self.delimiter is not None:
   __lowerCAmelCase											=						self.delimiter
  if self.column_names is not None:
   __lowerCAmelCase											=						self.column_names





 @property
 def a					(      self   :	Union[str, Any]							)  ->    str:
  __lowerCAmelCase											=						{
      """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()      ,       SCREAMING_SNAKE_CASE__							):
    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 				_lowercase     (				datasets.ArrowBasedBuilder			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Any      	=					CsvConfig
 def a					(      self   :	Dict							)  ->    Optional[Any]:
  return datasets.DatasetInfo(features=self.config.features							)
 def a					(      self   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	Tuple							)  ->    Optional[int]:
  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											=						dl_manager.download_and_extract(self.config.data_files							)
  if isinstance(SCREAMING_SNAKE_CASE__      ,       (str, list, tuple)							):
   __lowerCAmelCase											=						data_files
   if isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
    __lowerCAmelCase											=						[files]
   __lowerCAmelCase											=						[dl_manager.iter_files(SCREAMING_SNAKE_CASE__							) for file in files]
   return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,       gen_kwargs={"""files""": files}							)]
  __lowerCAmelCase											=						[]
  for split_name, files in data_files.items():
   if isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
    __lowerCAmelCase											=						[files]
   __lowerCAmelCase											=						[dl_manager.iter_files(SCREAMING_SNAKE_CASE__							) for file in files]
   splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE__      ,       gen_kwargs={"""files""": files}							)							)
  return splits
 def a					(      self   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	pa.Table							)  ->    pa.Table:
  if self.config.features is not None:
   __lowerCAmelCase											=						self.config.features.arrow_schema
   if all(not require_storage_cast(SCREAMING_SNAKE_CASE__							) for feature in self.config.features.values()							):
    # cheaper cast
    __lowerCAmelCase											=						pa.Table.from_arrays([pa_table[field.name] for field in schema]      ,       schema=SCREAMING_SNAKE_CASE__							)
   else:
    # more expensive cast; allows str <-> int/float or str to Audio for example
    __lowerCAmelCase											=						table_cast(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
  return pa_table





 def a					(      self   :	int      ,       SCREAMING_SNAKE_CASE__   :	int							)  ->    Optional[int]:
  __lowerCAmelCase											=						self.config.features.arrow_schema if self.config.features else None
  # dtype allows reading an int column as str
  __lowerCAmelCase											=						(
      {
          name: dtype.to_pandas_dtype() if not require_storage_cast(SCREAMING_SNAKE_CASE__							) 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(SCREAMING_SNAKE_CASE__							)							):
   __lowerCAmelCase											=						pd.read_csv(SCREAMING_SNAKE_CASE__      ,       iterator=SCREAMING_SNAKE_CASE__      ,       dtype=SCREAMING_SNAKE_CASE__      ,       **self.config.pd_read_csv_kwargs							)
   try:
    for batch_idx, df in enumerate(SCREAMING_SNAKE_CASE__							):
     __lowerCAmelCase											=						pa.Table.from_pandas(SCREAMING_SNAKE_CASE__							)
     # 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(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_						(					snake_case_       :       Union[str, Any]=2_81_23      )					->     str:






 '''simple docstring'''

 __lowerCAmelCase											=						[1] * (limit + 1)

 for i in range(2						,		int(limit**0.5      ) + 1      ):
  sum_divs[i * i] += i
  for k in range(i + 1						,		limit // i + 1      ):
   sum_divs[k * i] += k + i

 __lowerCAmelCase											=						set()
 __lowerCAmelCase											=						0

 for n in range(1						,		limit + 1      ):
  if sum_divs[n] > n:
   abundants.add(snake_case_      )

  if not any((n - a in abundants) for a in abundants      ):
   res += n

 return res


if __name__ == "__main__":
 print(solution())






