





'''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


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

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

@dataclass
class    lowerCAmelCase_		(		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         =					10_000
 __lowerCamelCase						:							Optional[datasets.Features]         =					None
 __lowerCamelCase						:							Optional[str]         =					"strict"
 __lowerCamelCase						:							Literal["error", "warn", "skip"]         =					"error"
 __lowerCamelCase						:							Optional[str]         =					None




 def 							_snake_case   (	self		)    ->							int:

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





 @property
 def 							_snake_case   (	self		)    ->							List[Any]:
     _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    lowerCAmelCase_		(		datasets.ArrowBasedBuilder		):
 __lowerCamelCase						:							List[Any]         =					CsvConfig




 def 							_snake_case   (	self		)    ->							Optional[int]:
     return datasets.DatasetInfo(features=self.config.features		)




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							str:

     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 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							List[Any]:

     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 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Dict:
     _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'''


import math


def 	__a(SCREAMING_SNAKE_CASE_  :					int = 100    ):



    '''simple docstring'''





    _lowerCAmelCase				=      sum(i * i for i in range(1		,       n + 1    )    )
    _lowerCAmelCase				=      int(math.pow(sum(range(1		,       n + 1    )    )		,       2    )    )
    return square_of_sum - sum_of_squares


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


