


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   __a			(    __UpperCamelCase      ):
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__ = False				, lowerCAmelCase__ = False				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, **lowerCAmelCase__				, )					->				List[str]:




        '''simple docstring'''
        super().__init__(
            lowerCAmelCase__				, split=lowerCAmelCase__				, features=lowerCAmelCase__				, cache_dir=lowerCAmelCase__				, keep_in_memory=lowerCAmelCase__				, streaming=lowerCAmelCase__				, num_proc=lowerCAmelCase__				, **lowerCAmelCase__				, )
        lowercase__:   List[Any]  			=   field
        lowercase__:   Optional[Any]  			=   path_or_paths if isinstance(lowerCAmelCase__				, lowerCAmelCase__ ) else {self.split: path_or_paths}
        lowercase__:   Optional[Any]  			=   Json(
            cache_dir=lowerCAmelCase__				, data_files=lowerCAmelCase__				, features=lowerCAmelCase__				, field=lowerCAmelCase__				, **lowerCAmelCase__				, )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				str:




        '''simple docstring'''
        # Build iterable dataset
        if self.streaming:
               lowercase__:   Union[str, Any]  			=   self.builder.as_streaming_dataset(split=self.split )
        # Build regular (map-style) dataset
        else:
               lowercase__:   Tuple  			=   None
               lowercase__:   int  			=   None
               lowercase__:   List[str]  			=   None
               lowercase__:   int  			=   None

               self.builder.download_and_prepare(
                   download_config=lowerCAmelCase__				, download_mode=lowerCAmelCase__				, verification_mode=lowerCAmelCase__				, base_path=lowerCAmelCase__				, num_proc=self.num_proc				, )
               lowercase__:   List[Any]  			=   self.builder.as_dataset(
                   split=self.split				, verification_mode=lowerCAmelCase__				, in_memory=self.keep_in_memory )
        return dataset





class   __a			:
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, **lowerCAmelCase__				, )					->				Optional[int]:




        '''simple docstring'''
        if num_proc is not None and num_proc <= 0:
               raise ValueError(F'num_proc {num_proc} must be an integer > 0.' )

        lowercase__:   str  			=   dataset
        lowercase__:   List[str]  			=   path_or_buf
        lowercase__:   Dict  			=   batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        lowercase__:   List[str]  			=   num_proc
        lowercase__:   List[Any]  			=   'utf-8'
        lowercase__:   Optional[int]  			=   to_json_kwargs
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				int:




        '''simple docstring'''
        lowercase__:   Any  			=   self.to_json_kwargs.pop('path_or_buf'				, lowerCAmelCase__ )
        lowercase__:   Optional[int]  			=   self.to_json_kwargs.pop('orient'				, 'records' )
        lowercase__:   Dict  			=   self.to_json_kwargs.pop('lines'				, True if orient == 'records' else False )
        lowercase__:   Dict  			=   self.to_json_kwargs.pop('index'				, False if orient in ['split', 'table'] else True )
        lowercase__:   Tuple  			=   self.to_json_kwargs.pop('compression'				, lowerCAmelCase__ )

        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=lowerCAmelCase__ ) as buffer:
                      lowercase__:   Dict  			=   self._write(file_obj=lowerCAmelCase__				, orient=lowerCAmelCase__				, lines=lowerCAmelCase__				, index=lowerCAmelCase__				, **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.' )
               lowercase__:   List[str]  			=   self._write(
                   file_obj=self.path_or_buf				, orient=lowerCAmelCase__				, lines=lowerCAmelCase__				, index=lowerCAmelCase__				, **self.to_json_kwargs )
        return written
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				List[Any]:




        '''simple docstring'''
        lowercase__			,						lowercase__			,						lowercase__			,						lowercase__			,						lowercase__:   Optional[int]  			=   args

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




        '''simple docstring'''
        lowercase__:   Dict  			=   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'				, ):
                      lowercase__:   Any  			=   self._batch_json((offset, orient, lines, index, to_json_kwargs) )
                      written += file_obj.write(lowerCAmelCase__ )
        else:
               lowercase__			,						lowercase__:   str  			=   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				, lowerCAmelCase__				, lowerCAmelCase__ )]				, )				, 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(lowerCAmelCase__ )

        return written






def    snake_case_     (  snake_case			,							snake_case     )  ->				bool:
       return numa ^ numa < 0


if __name__ == "__main__":
 import doctest

 doctest.testmod()


