






import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = False				,		_UpperCamelCase = False				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		**_UpperCamelCase				,		)	->							Optional[int]:
							super().__init__(
							    _UpperCamelCase				,		split=_UpperCamelCase				,		features=_UpperCamelCase				,		cache_dir=_UpperCamelCase				,		keep_in_memory=_UpperCamelCase				,		streaming=_UpperCamelCase				,		num_proc=_UpperCamelCase				,		**_UpperCamelCase				,		)
							UpperCAmelCase_    :      Any            =						field
							UpperCAmelCase_    :      Tuple            =						path_or_paths if isinstance(_UpperCamelCase				,		_UpperCamelCase   ) else {self.split: path_or_paths}
							UpperCAmelCase_    :      int            =						Json(
							    cache_dir=_UpperCamelCase				,		data_files=_UpperCamelCase				,		features=_UpperCamelCase				,		field=_UpperCamelCase				,		**_UpperCamelCase				,		)



		def 					__UpperCAmelCase			(							self   )	->							List[Any]:
							# Build iterable dataset
							if self.streaming:
												UpperCAmelCase_    :      Optional[Any]            =						self.builder.as_streaming_dataset(split=self.split   )
							# Build regular (map-style) dataset
							else:
												UpperCAmelCase_    :      Optional[int]            =						None
												UpperCAmelCase_    :      List[Any]            =						None
												UpperCAmelCase_    :      Any            =						None
												UpperCAmelCase_    :      Any            =						None

												self.builder.download_and_prepare(
												    download_config=_UpperCamelCase				,		download_mode=_UpperCamelCase				,		verification_mode=_UpperCamelCase				,		base_path=_UpperCamelCase				,		num_proc=self.num_proc				,		)
												UpperCAmelCase_    :      List[str]            =						self.builder.as_dataset(
												    split=self.split				,		verification_mode=_UpperCamelCase				,		in_memory=self.keep_in_memory   )
							return dataset





class   lowerCamelCase      :







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		**_UpperCamelCase				,		)	->							Union[str, Any]:
							if num_proc is not None and num_proc <= 0:
												raise ValueError(f"num_proc {num_proc} must be an integer > 0."   )

							UpperCAmelCase_    :      List[Any]            =						dataset
							UpperCAmelCase_    :      Optional[int]            =						path_or_buf
							UpperCAmelCase_    :      List[str]            =						batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
							UpperCAmelCase_    :      str            =						num_proc
							UpperCAmelCase_    :      Optional[int]            =						'utf-8'
							UpperCAmelCase_    :      Optional[int]            =						to_json_kwargs



		def 					__UpperCAmelCase			(							self   )	->							int:
							UpperCAmelCase_    :      Dict            =						self.to_json_kwargs.pop('path_or_buf'				,		_UpperCamelCase   )
							UpperCAmelCase_    :      Union[str, Any]            =						self.to_json_kwargs.pop('orient'				,		'records'   )
							UpperCAmelCase_    :      Any            =						self.to_json_kwargs.pop('lines'				,		True if orient == 'records' else False   )
							UpperCAmelCase_    :      str            =						self.to_json_kwargs.pop('index'				,		False if orient in ['split', 'table'] else True   )
							UpperCAmelCase_    :      List[str]            =						self.to_json_kwargs.pop('compression'				,		_UpperCamelCase   )

							if compression not in [None, "infer", "gzip", "bz2", "xz"]:
												raise NotImplementedError(f"`datasets` currently does not support {compression} compression"   )

							if isinstance(self.path_or_buf				,		(str, bytes, os.PathLike)   ):
												with fsspec.open(self.path_or_buf				,		'wb'				,		compression=_UpperCamelCase   ) as buffer:
																	UpperCAmelCase_    :      Any            =						self._write(file_obj=_UpperCamelCase				,		orient=_UpperCamelCase				,		lines=_UpperCamelCase				,		index=_UpperCamelCase				,		**self.to_json_kwargs   )
							else:
												if compression:
																	raise NotImplementedError(
																	    f"The compression parameter is not supported when writing to a buffer, but compression={compression}"
																	    ' was passed. Please provide a local path instead.'   )
												UpperCAmelCase_    :      Tuple            =						self._write(
												    file_obj=self.path_or_buf				,		orient=_UpperCamelCase				,		lines=_UpperCamelCase				,		index=_UpperCamelCase				,		**self.to_json_kwargs   )
							return written



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							List[Any]:
							UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      List[str]            =						args

							UpperCAmelCase_    :      List[str]            =						query_table(
							    table=self.dataset.data				,		key=slice(_UpperCamelCase				,		offset + self.batch_size   )				,		indices=self.dataset._indices				,		)
							UpperCAmelCase_    :      str            =						batch.to_pandas().to_json(
							    path_or_buf=_UpperCamelCase				,		orient=_UpperCamelCase				,		lines=_UpperCamelCase				,		index=_UpperCamelCase				,		**_UpperCamelCase   )
							if not json_str.endswith('\n'   ):
												json_str += "\n"
							return json_str.encode(self.encoding   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		**_UpperCamelCase				,		)	->							int:
							UpperCAmelCase_    :      Any            =						0

							if self.num_proc is None or self.num_proc == 1:
												for offset in logging.tqdm(
												    range(0				,		len(self.dataset   )				,		self.batch_size   )				,		unit='ba'				,		disable=not logging.is_progress_bar_enabled()				,		desc='Creating json from Arrow format'				,		):
																	UpperCAmelCase_    :      List[Any]            =						self._batch_json((offset, orient, lines, index, to_json_kwargs)   )
																	written += file_obj.write(_UpperCamelCase   )
							else:
												UpperCAmelCase_       ,  UpperCAmelCase_    :      List[Any]            =						len(self.dataset   ), self.batch_size
												with multiprocessing.Pool(self.num_proc   ) as pool:
																	for json_str in logging.tqdm(
																	    pool.imap(
																	        self._batch_json				,		[(offset, orient, lines, index, to_json_kwargs) for offset in range(0				,		_UpperCamelCase				,		_UpperCamelCase   )]				,		)				,		total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size				,		unit='ba'				,		disable=not logging.is_progress_bar_enabled()				,		desc='Creating json from Arrow format'				,		):
																						written += file_obj.write(_UpperCamelCase   )

							return written














import os

# Precomputes a list of the 100 first triangular numbers
__UpperCAmelCase								=						[int(0.5 * n * (n + 1)) for n in range(1, 101)]




def      lowercase__      (     ):


					'''simple docstring'''


					UpperCAmelCase_    :      Any            =						os.path.dirname(os.path.realpath(__snake_case  )  )
					UpperCAmelCase_    :      Optional[Any]            =						os.path.join(__snake_case     ,       'words.txt'  )

					UpperCAmelCase_    :      Union[str, Any]            =						''
					with open(__snake_case  ) as f:
										UpperCAmelCase_    :      List[Any]            =						f.readline()

					UpperCAmelCase_    :      Optional[int]            =						[word.strip('"'  ) for word in words.strip('\r\n'  ).split(','  )]
					UpperCAmelCase_    :      Optional[int]            =						[
					    word
					    for word in [sum(ord(__snake_case  ) - 64 for x in word  ) for word in words]
					    if word in TRIANGULAR_NUMBERS
					]
					return len(__snake_case  )


if __name__ == "__main__":
		print(solution())






