



"""simple docstring"""

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        _UpperCAmelCase       (			lowerCAmelCase__):

 def __init__(     self		:       Optional[int]  ,     lowercase_		:       NestedDataStructureLike[PathLike]  ,     lowercase_		:       Optional[NamedSplit] = None  ,     lowercase_		:       Optional[Features] = None  ,     lowercase_		:       str = None  ,     lowercase_		:       bool = False  ,     lowercase_		:       bool = False  ,     lowercase_		:       Optional[str] = None  ,     lowercase_		:       Optional[int] = None  ,     **lowercase_		:       Any  ,     ):
     super().__init__(
         lowercase_  ,     split=lowercase_  ,     features=lowercase_  ,     cache_dir=lowercase_  ,     keep_in_memory=lowercase_  ,     streaming=lowercase_  ,     num_proc=lowercase_  ,     **lowercase_  ,     )
     snake_case_ :     Dict				=	field
     snake_case_ :     Optional[int]				=	path_or_paths if isinstance(lowercase_  ,     lowercase_				) else {self.split: path_or_paths}
     snake_case_ :     List[Any]				=	Json(
         cache_dir=lowercase_  ,     data_files=lowercase_  ,     features=lowercase_  ,     field=lowercase_  ,     **lowercase_  ,     )






 def 						_snake_case					(     self		:       str				):
     # Build iterable dataset
     if self.streaming:
         snake_case_ :     str				=	self.builder.as_streaming_dataset(split=self.split				)
     # Build regular (map-style) dataset
     else:
         snake_case_ :     Union[str, Any]				=	None
         snake_case_ :     Optional[int]				=	None
         snake_case_ :     List[str]				=	None
         snake_case_ :     str				=	None

         self.builder.download_and_prepare(
             download_config=lowercase_  ,     download_mode=lowercase_  ,     verification_mode=lowercase_  ,     base_path=lowercase_  ,     num_proc=self.num_proc  ,     )
         snake_case_ :     Tuple				=	self.builder.as_dataset(
             split=self.split  ,     verification_mode=lowercase_  ,     in_memory=self.keep_in_memory				)
     return dataset





class        _UpperCAmelCase       :

 def __init__(     self		:       Any  ,     lowercase_		:       Dataset  ,     lowercase_		:       Union[PathLike, BinaryIO]  ,     lowercase_		:       Optional[int] = None  ,     lowercase_		:       Optional[int] = None  ,     **lowercase_		:       Optional[Any]  ,     ):
     if num_proc is not None and num_proc <= 0:
         raise ValueError(f"num_proc {num_proc} must be an integer > 0."				)

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

 def 						_snake_case					(     self		:       List[str]				):
     snake_case_ :     Optional[int]				=	self.to_json_kwargs.pop('''path_or_buf'''  ,     lowercase_				)
     snake_case_ :     List[Any]				=	self.to_json_kwargs.pop('''orient'''  ,     '''records'''				)
     snake_case_ :     str				=	self.to_json_kwargs.pop('''lines'''  ,     True if orient == '''records''' else False				)
     snake_case_ :     Union[str, Any]				=	self.to_json_kwargs.pop('''index'''  ,     False if orient in ['''split''', '''table'''] else True				)
     snake_case_ :     List[str]				=	self.to_json_kwargs.pop('''compression'''  ,     lowercase_				)

     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=lowercase_				) as buffer:
             snake_case_ :     str				=	self._write(file_obj=lowercase_  ,     orient=lowercase_  ,     lines=lowercase_  ,     index=lowercase_  ,     **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.'''				)
         snake_case_ :     List[str]				=	self._write(
             file_obj=self.path_or_buf  ,     orient=lowercase_  ,     lines=lowercase_  ,     index=lowercase_  ,     **self.to_json_kwargs				)
     return written

 def 						_snake_case					(     self		:       Union[str, Any]  ,     lowercase_		:       int				):
     snake_case_,	snake_case_,	snake_case_,	snake_case_,	snake_case_ :     Tuple				=	args

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






 def 						_snake_case					(     self		:       int  ,     lowercase_		:       BinaryIO  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       str  ,     lowercase_		:       int  ,     **lowercase_		:       Any  ,     ):
     snake_case_ :     List[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'''  ,     ):
             snake_case_ :     List[Any]				=	self._batch_json((offset, orient, lines, index, to_json_kwargs)				)
             written += file_obj.write(lowercase_				)
     else:
         snake_case_,	snake_case_ :     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  ,     lowercase_  ,     lowercase_				)]  ,     )  ,     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(lowercase_				)

     return written










"""simple docstring"""

def __lowercase			(							_a		,			_a		,			_a=False    ):

    if isinstance(_a		,			_a    ) and isinstance(_a		,			_a    ):
        snake_case_ :     Union[str, Any]				=	len(set_a.intersection(_a    )    )

        if alternative_union:
            snake_case_ :     Any				=	len(_a    ) + len(_a    )
        else:
            snake_case_ :     str				=	len(set_a.union(_a    )    )

        return intersection / union

    if isinstance(_a		,			(list, tuple)    ) and isinstance(_a		,			(list, tuple)    ):
        snake_case_ :     str				=	[element for element in set_a if element in set_b]

        if alternative_union:
            snake_case_ :     Tuple				=	len(_a    ) + len(_a    )
            return len(_a    ) / union
        else:
            snake_case_ :     List[Any]				=	set_a + [element for element in set_b if element not in set_a]
            return len(_a    ) / len(_a    )

        return len(_a    ) / len(_a    )
    return None


if __name__ == "__main__":
     lowercase__    :  Any							    =	{'''a''', '''b''', '''c''', '''d''', '''e'''}
     lowercase__    :  Optional[Any]							    =	{'''c''', '''d''', '''e''', '''f''', '''h''', '''i'''}
     print(jaccard_similarity(set_a, set_b))





