




import itertools
from dataclasses import dataclass
from io import StringIO
from typing import Optional

import pyarrow as pa

import datasets
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


__a  :		Union[str, Any]		     =   datasets.utils.logging.get_logger(__name__)







@dataclass
class 				UpperCAmelCase(					datasets.BuilderConfig					):



	"""simple docstring"""






	a     : Optional[datasets.Features]	  =					None
	a     : str	  =					"utf-8"
	a     : Optional[str]	  =					None
	a     : int	  =					1_0 << 2_0  # 10MB
	a     : bool	  =					False
	a     : str	  =					"line"







class 				UpperCAmelCase(					datasets.ArrowBasedBuilder					):



	"""simple docstring"""






	a     : int	  =					TextConfig

	def    __a     (					self    ) ->      int:





								"""simple docstring"""


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

	def    __a     (					self	,				lowerCamelCase    ) ->      str:





								"""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}"""    )
								lowercase__  :	int        =							dl_manager.download_and_extract(self.config.data_files    )
								if isinstance(lowerCamelCase	,				(str, list, tuple)    ):
															lowercase__  :	Tuple        =							data_files
															if isinstance(lowerCamelCase	,				lowerCamelCase    ):
																						lowercase__  :	Union[str, Any]        =							[files]
															lowercase__  :	Optional[int]        =							[dl_manager.iter_files(lowerCamelCase    ) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,				gen_kwargs={"files": files}    )]
								lowercase__  :	List[Any]        =							[]
								for split_name, files in data_files.items():
															if isinstance(lowerCamelCase	,				lowerCamelCase    ):
																						lowercase__  :	Any        =							[files]
															lowercase__  :	List[Any]        =							[dl_manager.iter_files(lowerCamelCase    ) for file in files]
															splits.append(datasets.SplitGenerator(name=lowerCamelCase	,				gen_kwargs={"files": files}    )    )
								return splits

	def    __a     (					self	,				lowerCamelCase    ) ->      pa.Table:





								"""simple docstring"""


								if self.config.features is not None:
															lowercase__  :	Optional[Any]        =							self.config.features.arrow_schema
															if all(not require_storage_cast(lowerCamelCase    ) for feature in self.config.features.values()    ):
																						# cheaper cast
																						lowercase__  :	int        =							pa_table.cast(lowerCamelCase    )
															else:
																						# more expensive cast; allows str <-> int/float or str to Audio for example
																						lowercase__  :	str        =							table_cast(lowerCamelCase	,				lowerCamelCase    )
															return pa_table
								else:
															return pa_table.cast(pa.schema({"text": pa.string()}    )    )







	def    __a     (					self	,				lowerCamelCase    ) ->      str:





								"""simple docstring"""


								lowercase__  :	Optional[int]        =							list(self.config.features    ) if self.config.features is not None else ["text"]
								for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCamelCase    )    ):
															# open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
															with open(lowerCamelCase	,				encoding=self.config.encoding	,				errors=self.config.errors    ) as f:
																						if self.config.sample_by == "line":
																													lowercase__  :	Tuple        =							0
																													while True:
																																				lowercase__  :	Tuple        =							f.read(self.config.chunksize    )
																																				if not batch:
																																											break
																																				batch += f.readline()  # finish current line
																																				# StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
																																				lowercase__  :	Optional[Any]        =							StringIO(lowerCamelCase    ).readlines()
																																				if not self.config.keep_linebreaks:
																																											lowercase__  :	Dict        =							[line.rstrip("\n"    ) for line in batch]
																																				lowercase__  :	Any        =							pa.Table.from_arrays([pa.array(lowerCamelCase    )]	,				names=lowerCamelCase    )
																																				# 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(lowerCamelCase    )
																																				batch_idx += 1
																						elif self.config.sample_by == "paragraph":
																													lowercase__  :	Dict        =							0
																													lowercase__  :	Union[str, Any]        =							""
																													while True:
																																				lowercase__  :	Dict        =							f.read(self.config.chunksize    )
																																				if not new_batch:
																																											break
																																				batch += new_batch
																																				batch += f.readline()  # finish current line
																																				lowercase__  :	List[str]        =							batch.split("\n\n"    )
																																				lowercase__  :	List[Any]        =							pa.Table.from_arrays(
																																				    [pa.array([example for example in batch[:-1] if example]    )]	,				names=lowerCamelCase    )
																																				# 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(lowerCamelCase    )
																																				batch_idx += 1
																																				lowercase__  :	str        =							batch[-1]
																													if batch:
																																				lowercase__  :	Tuple        =							pa.Table.from_arrays([pa.array([batch]    )]	,				names=lowerCamelCase    )
																																				yield (file_idx, batch_idx), self._cast_table(lowerCamelCase    )
																						elif self.config.sample_by == "document":
																													lowercase__  :	Any        =							f.read()
																													lowercase__  :	Optional[Any]        =							pa.Table.from_arrays([pa.array([text]    )]	,				names=lowerCamelCase    )
																													yield file_idx, self._cast_table(lowerCamelCase    )





from __future__ import annotations

import math
import random
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			list[int]:
							lowercase__  :	List[Any]        =							d * e - 1
							lowercase__  :	Any        =							0
							lowercase__  :	Dict        =							0
							while p == 0:
														lowercase__  :	Dict        =							random.randint(2    ,n - 1    )
														lowercase__  :	int        =							k
														while True:
																					if t % 2 == 0:
																												lowercase__  :	str        =							t // 2
																												lowercase__  :	int        =							(g**t) % n
																												lowercase__  :	List[Any]        =							math.gcd(x - 1    ,SCREAMING_SNAKE_CASE_    )
																												if x > 1 and y > 1:
																																			lowercase__  :	Any        =							y
																																			lowercase__  :	Optional[int]        =							n // y
																																			break  # find the correct factors
																					else:
																												break  # t is not divisible by 2, break and choose another g
							return sorted([p, q]    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()