






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(	UpperCAmelCase_ ):





       """simple docstring"""







       def __init__( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = False  ,      _lowerCamelCase = False  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
        super().__init__(
            _lowerCamelCase  ,      split=_lowerCamelCase  ,      features=_lowerCamelCase  ,      cache_dir=_lowerCamelCase  ,      keep_in_memory=_lowerCamelCase  ,      streaming=_lowerCamelCase  ,      num_proc=_lowerCamelCase  ,      **_lowerCamelCase  ,      )
        UpperCamelCase_: List[Any]   				=    field
        UpperCamelCase_: List[Any]   				=    path_or_paths if isinstance(_lowerCamelCase  ,      _lowerCamelCase							) else {self.split: path_or_paths}
        UpperCamelCase_: Any   				=    Json(
            cache_dir=_lowerCamelCase  ,      data_files=_lowerCamelCase  ,      features=_lowerCamelCase  ,      field=_lowerCamelCase  ,      **_lowerCamelCase  ,      )





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

         self.builder.download_and_prepare(
             download_config=_lowerCamelCase  ,      download_mode=_lowerCamelCase  ,      verification_mode=_lowerCamelCase  ,      base_path=_lowerCamelCase  ,      num_proc=self.num_proc  ,      )
         UpperCamelCase_: Optional[Any]   				=    self.builder.as_dataset(
             split=self.split  ,      verification_mode=_lowerCamelCase  ,      in_memory=self.keep_in_memory							)
        return dataset







class     _lowerCAmelCase:





       """simple docstring"""







       def __init__( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
        if num_proc is not None and num_proc <= 0:
         raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''							)

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





       def       _a   ( self							):
        UpperCamelCase_: Dict   				=    self.to_json_kwargs.pop('path_or_buf'  ,      _lowerCamelCase							)
        UpperCamelCase_: List[Any]   				=    self.to_json_kwargs.pop('orient'  ,      'records'							)
        UpperCamelCase_: List[Any]   				=    self.to_json_kwargs.pop('lines'  ,      True if orient == 'records' else False							)
        UpperCamelCase_: List[str]   				=    self.to_json_kwargs.pop('index'  ,      False if orient in ['split', 'table'] else True							)
        UpperCamelCase_: List[Any]   				=    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:
          UpperCamelCase_: str   				=    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.'							)
         UpperCamelCase_: List[Any]   				=    self._write(
             file_obj=self.path_or_buf  ,      orient=_lowerCamelCase  ,      lines=_lowerCamelCase  ,      index=_lowerCamelCase  ,      **self.to_json_kwargs							)
        return written





       def       _a   ( self  ,      _lowerCamelCase							):
        UpperCamelCase_: str   				=    args

        UpperCamelCase_: List[Any]   				=    query_table(
            table=self.dataset.data  ,      key=slice(_lowerCamelCase  ,      offset + self.batch_size							)  ,      indices=self.dataset._indices  ,      )
        UpperCamelCase_: 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       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      **_lowerCamelCase  ,      ):
        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_: Any   				=    self._batch_json((offset, orient, lines, index, to_json_kwargs)							)
          written += file_obj.write(_lowerCamelCase							)
        else:
         UpperCamelCase_: Optional[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  ,      _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







from abc import ABC, abstractmethod
from argparse import ArgumentParser




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       @staticmethod
       @abstractmethod
       def       _a   ( _lowerCamelCase							):
         raise NotImplementedError()





       @abstractmethod
       def       _a   ( self							):
         raise NotImplementedError()