






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      	=  datasets.utils.logging.get_logger(__name__)

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





@dataclass
class __lowerCamelCase (      datasets.BuilderConfig):





	"""simple docstring"""






	UpperCamelCase__								=      ","
	UpperCamelCase__								=      None
	UpperCamelCase__								=      "infer"
	UpperCamelCase__								=      None
	UpperCamelCase__								=      None
	UpperCamelCase__								=      None
	UpperCamelCase__								=      None
	UpperCamelCase__								=      None
	UpperCamelCase__								=      True
	UpperCamelCase__								=      None
	UpperCamelCase__								=      None
	UpperCamelCase__								=      None
	UpperCamelCase__								=      None
	UpperCamelCase__								=      False
	UpperCamelCase__								=      None
	UpperCamelCase__								=      None
	UpperCamelCase__								=      None
	UpperCamelCase__								=      True
	UpperCamelCase__								=      True
	UpperCamelCase__								=      False
	UpperCamelCase__								=      True
	UpperCamelCase__								=      None
	UpperCamelCase__								=      "."
	UpperCamelCase__								=      None
	UpperCamelCase__								=      '"'
	UpperCamelCase__								=      0
	UpperCamelCase__								=      None
	UpperCamelCase__								=      None
	UpperCamelCase__								=      None
	UpperCamelCase__								=      None
	UpperCamelCase__								=      True
	UpperCamelCase__								=      True
	UpperCamelCase__								=      0
	UpperCamelCase__								=      True
	UpperCamelCase__								=      False
	UpperCamelCase__								=      None
	UpperCamelCase__								=      1_0000
	UpperCamelCase__								=      None
	UpperCamelCase__								=      "strict"
	UpperCamelCase__								=      "error"
	UpperCamelCase__								=      None

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

	@property
	def        UpperCamelCase    (							self    ):
							"""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()       ,    UpperCAmelCase    ):
																			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):





	"""simple docstring"""






	UpperCamelCase__								=      CsvConfig

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return datasets.DatasetInfo(features=self.config.features    )

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

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""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(UpperCAmelCase    ) 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(UpperCAmelCase    )    ):
													_UpperCAmelCase									=			pd.read_csv(UpperCAmelCase       ,    iterator=UpperCAmelCase       ,    dtype=UpperCAmelCase       ,    **self.config.pd_read_csv_kwargs    )
													try:
																			for batch_idx, df in enumerate(UpperCAmelCase    ):
																									_UpperCAmelCase									=			pa.Table.from_pandas(UpperCAmelCase    )
																									# 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(UpperCAmelCase    )
													except ValueError as e:
																			logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase    )}: {e}"""    )
																			raise








from __future__ import annotations


def __A    (      __lowerCAmelCase	)->  list[int]:





						"""simple docstring"""



						_UpperCAmelCase									=			2
						_UpperCAmelCase									=			[]
						while i * i <= n:
												if n % i:
																		i += 1
												else:
																		n //= i
																		factors.append(__lowerCAmelCase	)
						if n > 1:
												factors.append(__lowerCAmelCase	)
						return factors


if __name__ == "__main__":
		import doctest

		doctest.testmod()
