






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:     Any   					=					datasets.utils.logging.get_logger(__name__)

__a:     str   					=					["""names""", """prefix"""]
__a:     str   					=					["""warn_bad_lines""", """error_bad_lines""", """mangle_dupe_cols"""]
__a:     Any   					=					["""encoding_errors""", """on_bad_lines"""]
__a:     Any   					=					["""date_format"""]
@dataclass
class 		SCREAMING_SNAKE_CASE__	(  datasets.BuilderConfig     ):

  '''simple docstring'''







  _lowerCamelCase    =  ''','''
  _lowerCamelCase    =  None
  _lowerCamelCase    =  '''infer'''
  _lowerCamelCase    =  None
  _lowerCamelCase    =  None
  _lowerCamelCase    =  None
  _lowerCamelCase    =  None
  _lowerCamelCase    =  None
  _lowerCamelCase    =  True
  _lowerCamelCase    =  None
  _lowerCamelCase    =  None
  _lowerCamelCase    =  None
  _lowerCamelCase    =  None
  _lowerCamelCase    =  False
  _lowerCamelCase    =  None
  _lowerCamelCase    =  None
  _lowerCamelCase    =  None
  _lowerCamelCase    =  True
  _lowerCamelCase    =  True
  _lowerCamelCase    =  False
  _lowerCamelCase    =  True
  _lowerCamelCase    =  None
  _lowerCamelCase    =  '''.'''
  _lowerCamelCase    =  None
  _lowerCamelCase    =  '''"'''
  _lowerCamelCase    =  0
  _lowerCamelCase    =  None
  _lowerCamelCase    =  None
  _lowerCamelCase    =  None
  _lowerCamelCase    =  None
  _lowerCamelCase    =  True
  _lowerCamelCase    =  True
  _lowerCamelCase    =  0
  _lowerCamelCase    =  True
  _lowerCamelCase    =  False
  _lowerCamelCase    =  None
  _lowerCamelCase    =  10000
  _lowerCamelCase    =  None
  _lowerCamelCase    =  '''strict'''
  _lowerCamelCase    =  '''error'''
  _lowerCamelCase    =  None


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Tuple:


         """simple docstring"""

         if self.delimiter is not None:
                _UpperCAmelCase							=    self.delimiter
         if self.column_names is not None:
                _UpperCAmelCase							=    self.column_names





  @property
  def  lowerCamelCase       (      self		:			List[Any]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    {
             '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()       ,				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 		SCREAMING_SNAKE_CASE__	(  datasets.ArrowBasedBuilder     ):

  '''simple docstring'''







  _lowerCamelCase    =  CsvConfig


  def  lowerCamelCase       (      self		:			Tuple							)						->	Optional[Any]:


         """simple docstring"""
         return datasets.DatasetInfo(features=self.config.features							)


  def  lowerCamelCase       (      self		:			str       ,				lowerCamelCase		:			List[str]							)						->	Any:


         """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(snake_case__       ,				(str, list, tuple)							):
                _UpperCAmelCase							=    data_files
                if isinstance(snake_case__       ,				snake_case__							):
                       _UpperCAmelCase							=    [files]
                _UpperCAmelCase							=    [dl_manager.iter_files(snake_case__							) 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(snake_case__       ,				snake_case__							):
                       _UpperCAmelCase							=    [files]
                _UpperCAmelCase							=    [dl_manager.iter_files(snake_case__							) for file in files]
                splits.append(datasets.SplitGenerator(name=snake_case__       ,				gen_kwargs={"""files""": files}							)							)
         return splits


  def  lowerCamelCase       (      self		:			str       ,				lowerCamelCase		:			Any							)						->	List[str]:


         """simple docstring"""

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





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


         """simple docstring"""
         _UpperCAmelCase							=    self.config.features.arrow_schema if self.config.features else None
         # dtype allows reading an int column as str
         _UpperCAmelCase							=    (
             {
                 name: dtype.to_pandas_dtype() if not require_storage_cast(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(snake_case__							)							):
                _UpperCAmelCase							=    pd.read_csv(snake_case__       ,				iterator=snake_case__       ,				dtype=snake_case__       ,				**self.config.pd_read_csv_kwargs							)
                try:
                       for batch_idx, df in enumerate(snake_case__							):
                              _UpperCAmelCase							=    pa.Table.from_pandas(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(snake_case__							)
                except ValueError as e:
                       logger.error(f"""Failed to read file \'{file}\' with error {type(snake_case__							)}: {e}"""							)
                       raise



'''simple docstring'''







from __future__ import annotations

from math import gcd






def       lowercase					(_A ,    _A = 2 ,    _A = 1 ,    _A = 3 ,    ):







       """simple docstring"""







       if num < 2:
              raise ValueError('The input value cannot be less than 2'       )

       # Because of the relationship between ``f(f(x))`` and ``f(x)``, this
       # algorithm struggles to find factors that are divisible by two.
       # As a workaround, we specifically check for two and even inputs.
       #   See: https://math.stackexchange.com/a/2856214/165820
       if num > 2 and num % 2 == 0:
              return 2

       # Pollard's Rho algorithm requires a function that returns pseudorandom
       # values between 0 <= X < ``num``.  It doesn't need to be random in the
       # sense that the output value is cryptographically secure or difficult
       # to calculate, it only needs to be random in the sense that all output
       # values should be equally likely to appear.
       # For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
       # However, the success of Pollard's algorithm isn't guaranteed and is
       # determined in part by the initial seed and the chosen random function.
       # To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
       # where ``C`` is a value that we can modify between each attempt.
       def rand_fn(_A ,    _A ,    _A       ) -> int:
              return (pow(_A ,    2       ) + step) % modulus

       for _ in range(_A       ):
              # These track the position within the cycle detection logic.
              _lowerCAmelCase     :				Dict       =							seed
              _lowerCAmelCase     :				int       =							seed

              while True:
                     # At each iteration, the tortoise moves one step and the hare moves two.
                     _lowerCAmelCase     :				str       =							rand_fn(_A ,    _A ,    _A       )
                     _lowerCAmelCase     :				Optional[int]       =							rand_fn(_A ,    _A ,    _A       )
                     _lowerCAmelCase     :				Union[str, Any]       =							rand_fn(_A ,    _A ,    _A       )

                     # At some point both the tortoise and the hare will enter a cycle whose
                     # length ``p`` is a divisor of ``num``.  Once in that cycle, at some point
                     # the tortoise and hare will end up on the same value modulo ``p``.
                     # We can detect when this happens because the position difference between
                     # the tortoise and the hare will share a common divisor with ``num``.
                     _lowerCAmelCase     :				Optional[int]       =							gcd(hare - tortoise ,    _A       )

                     if divisor == 1:
                            # No common divisor yet, just keep searching.
                            continue
                     else:
                            # We found a common divisor!
                            if divisor == num:
                                   # Unfortunately, the divisor is ``num`` itself and is useless.
                                   break
                            else:
                                   # The divisor is a nontrivial factor of ``num``!
                                   return divisor

        # If we made it here, then this attempt failed.
        # We need to pick a new starting seed for the tortoise and hare
        # in addition to a new step value for the random function.
        # To keep this example implementation deterministic, the
        # new values will be generated based on currently available
        # values instead of using something like ``random.randint``.

        # We can use the hare's position as the new seed.
        # This is actually what Richard Brent's the "optimized" variant does.
              _lowerCAmelCase     :				Tuple       =							hare

              # The new step value for the random function can just be incremented.
              # At first the results will be similar to what the old function would
              # have produced, but the value will quickly diverge after a bit.
              step += 1

       # We haven't found a divisor within the requested number of attempts.
       # We were unlucky or ``num`` itself is actually prime.
       return None


if __name__ == "__main__":
       import argparse

       lowerCAmelCase     :  Tuple											=    argparse.ArgumentParser()
       parser.add_argument(
           """num""",
           type=int,
           help="""The value to find a divisor of""",
       )
       parser.add_argument(
           """--attempts""",
           type=int,
           default=3,
           help="""The number of attempts before giving up""",
       )
       lowerCAmelCase     :  List[str]											=    parser.parse_args()

       lowerCAmelCase     :  List[str]											=    pollard_rho(args.num, attempts=args.attempts)
       if divisor is None:
              print(F'''{args.num} is probably prime''')
       else:
              lowerCAmelCase     :  Union[str, Any]											=    args.num // divisor
              print(F'''{args.num} = {divisor} * {quotient}''')

