


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

snake_case							: List[Any]      =   ['''names''', '''prefix''']
snake_case							: List[Any]      =   ['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']
snake_case							: List[Any]      =   ['''encoding_errors''', '''on_bad_lines''']
snake_case							: str      =   ['''date_format''']







@dataclass
class  snake_case_     (datasets.BuilderConfig   ):
     UpperCAmelCase__      :					str					   =    ","
     UpperCAmelCase__      :					Optional[str]					   =    None
     UpperCAmelCase__      :					Optional[Union[int, List[int], str]]					   =    "infer"
     UpperCAmelCase__      :					Optional[List[str]]					   =    None
     UpperCAmelCase__      :					Optional[List[str]]					   =    None
     UpperCAmelCase__      :					Optional[Union[int, str, List[int], List[str]]]					   =    None
     UpperCAmelCase__      :					Optional[Union[List[int], List[str]]]					   =    None
     UpperCAmelCase__      :					Optional[str]					   =    None
     UpperCAmelCase__      :					bool					   =    True
     UpperCAmelCase__      :					Optional[Literal["c", "python", "pyarrow"]]					   =    None
     UpperCAmelCase__      :					Dict[Union[int, str], Callable[[Any], Any]]					   =    None
     UpperCAmelCase__      :					Optional[list]					   =    None
     UpperCAmelCase__      :					Optional[list]					   =    None
     UpperCAmelCase__      :					bool					   =    False
     UpperCAmelCase__      :					Optional[Union[int, List[int]]]					   =    None
     UpperCAmelCase__      :					Optional[int]					   =    None
     UpperCAmelCase__      :					Optional[Union[str, List[str]]]					   =    None
     UpperCAmelCase__      :					bool					   =    True
     UpperCAmelCase__      :					bool					   =    True
     UpperCAmelCase__      :					bool					   =    False
     UpperCAmelCase__      :					bool					   =    True
     UpperCAmelCase__      :					Optional[str]					   =    None
     UpperCAmelCase__      :					str					   =    "."
     UpperCAmelCase__      :					Optional[str]					   =    None
     UpperCAmelCase__      :					str					   =    '"'
     UpperCAmelCase__      :					int					   =    0
     UpperCAmelCase__      :					Optional[str]					   =    None
     UpperCAmelCase__      :					Optional[str]					   =    None
     UpperCAmelCase__      :					Optional[str]					   =    None
     UpperCAmelCase__      :					Optional[str]					   =    None
     UpperCAmelCase__      :					bool					   =    True
     UpperCAmelCase__      :					bool					   =    True
     UpperCAmelCase__      :					int					   =    0
     UpperCAmelCase__      :					bool					   =    True
     UpperCAmelCase__      :					bool					   =    False
     UpperCAmelCase__      :					Optional[str]					   =    None
     UpperCAmelCase__      :					int					   =    1_0_0_0_0
     UpperCAmelCase__      :					Optional[datasets.Features]					   =    None
     UpperCAmelCase__      :					Optional[str]					   =    "strict"
     UpperCAmelCase__      :					Literal["error", "warn", "skip"]					   =    "error"
     UpperCAmelCase__      :					Optional[str]					   =    None
     def 				lowerCamelCase__(   self     :str   )      ->					Optional[int]:
            if self.delimiter is not None:
                   a__    =  self.delimiter
            if self.column_names is not None:
                   a__    =  self.column_names



     @property
     def 				lowerCamelCase__(   self     :Tuple   )      ->					str:
            a__    =  {
                '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  snake_case_     (datasets.ArrowBasedBuilder   ):
     UpperCAmelCase__      :					Optional[Any]					   =    CsvConfig
     def 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					List[str]:
            return datasets.DatasetInfo(features=self.config.features   )
     def 				lowerCamelCase__(   self     :str    ,__snake_case     :Tuple   )      ->					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}'   )
            a__    =  dl_manager.download_and_extract(self.config.data_files   )
            if isinstance(__snake_case    ,(str, list, tuple)   ):
                   a__    =  data_files
                   if isinstance(__snake_case    ,__snake_case   ):
                          a__    =  [files]
                   a__    =  [dl_manager.iter_files(__snake_case   ) 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(__snake_case    ,__snake_case   ):
                          a__    =  [files]
                   a__    =  [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     :int    ,__snake_case     :pa.Table   )      ->					pa.Table:
            if self.config.features is not None:
                   a__    =  self.config.features.arrow_schema
                   if all(not require_storage_cast(__snake_case   ) for feature in self.config.features.values()   ):
                          # cheaper cast
                          a__    =  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
                          a__    =  table_cast(__snake_case    ,__snake_case   )
            return pa_table



     def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :Optional[Any]   )      ->					Dict:
            a__    =  self.config.features.arrow_schema if self.config.features else None
            # dtype allows reading an int column as str
            a__    =  (
                {
                    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   )   ):
                   a__    =  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   ):
                                 a__    =  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








from math import sqrt






def 							__lowercase     (				__lowerCAmelCase							:       int     ):

							if 1 < number < 4:
														# 2 and 3 are primes
														return True
							elif number < 2 or number % 2 == 0 or number % 3 == 0:
														# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
														return False

							# All primes number are in format of 6k +/- 1
							for i in range(5      ,					int(sqrt(__lowerCAmelCase     ) + 1     )      ,					6     ):
														if number % i == 0 or number % (i + 2) == 0:
																					return False
							return True






def 							__lowercase     (				__lowerCAmelCase							:       int = 1_0_0_0_1     ):
							a__    =  0
							a__    =  1
							while count != nth and number < 3:
														number += 1
														if is_prime(__lowerCAmelCase     ):
																					count += 1
							while count != nth:
														number += 2
														if is_prime(__lowerCAmelCase     ):
																					count += 1
							return number


if __name__ == "__main__":
						print(f"""{solution() = }""")




