"""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


__lowerCAmelCase    :							Tuple													=						datasets.utils.logging.get_logger(__name__)

__lowerCAmelCase    :							Dict													=						["names", "prefix"]
__lowerCAmelCase    :							Optional[int]													=						["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
__lowerCAmelCase    :							Optional[int]													=						["encoding_errors", "on_bad_lines"]
__lowerCAmelCase    :							Optional[Any]													=						["date_format"]

@dataclass
class       a_      (     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_0000
					UpperCamelCase_ :	Optional[datasets.Features]			=    None
					UpperCamelCase_ :	Optional[str]			=    "strict"
					UpperCamelCase_ :	Literal["error", "warn", "skip"]			=    "error"
					UpperCamelCase_ :	Optional[str]			=    None




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							if self.delimiter is not None:
									lowerCAmelCase__        =   self.delimiter
							if self.column_names is not None:
									lowerCAmelCase__        =   self.column_names




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[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()  ,						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       a_      (     datasets.ArrowBasedBuilder			):
					UpperCamelCase_ :	Dict			=    CsvConfig




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




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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any  ,						snake_case__						:     pa.Table     ):
							if self.config.features is not None:
									lowerCAmelCase__        =   self.config.features.arrow_schema
									if all(not require_storage_cast(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=snake_case__     )
									else:
											# more expensive cast; allows str <-> int/float or str to Audio for example
											lowerCAmelCase__        =   table_cast(snake_case__  ,						snake_case__     )
							return pa_table




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     Tuple     ):
							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(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__     )     ):
									lowerCAmelCase__        =   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__     ):
													lowerCAmelCase__        =   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 math import pi, sqrt







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		if num <= 0:
				raise ValueError("""math domain error"""     )
		if num > 1_71.5:
				raise OverflowError("""math range error"""     )
		elif num - int(lowerCamelCase__     ) not in (0, 0.5):
				raise NotImplementedError("""num must be an integer or a half-integer"""     )
		elif num == 0.5:
				return sqrt(lowerCamelCase__     )
		else:
				return 1.0 if num == 1 else (num - 1) * gamma(num - 1     )







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		assert gamma(0.5     ) == sqrt(lowerCamelCase__     )
		assert gamma(1     ) == 1.0
		assert gamma(2     ) == 1.0


if __name__ == "__main__":
			from doctest import testmod

			testmod()
			__lowerCAmelCase    :							Dict													=						1.0
			while num:
						__lowerCAmelCase    :							Any													=						float(input("Gamma of: "))
						print(F"gamma({num}) = {gamma(num)}")
						print("\nEnter 0 to exit...")




