





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	):




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase = None					,  lowercase = None					,  lowercase = None					,  lowercase = False					,  lowercase = False					,  lowercase = None					,  lowercase = None					,  **lowercase					,  )			->						Optional[int]:







          '''simple docstring'''

          super().__init__(
              lowercase					,  split=lowercase					,  features=lowercase					,  cache_dir=lowercase					,  keep_in_memory=lowercase					,  streaming=lowercase					,  num_proc=lowercase					,  **lowercase					,  )
          a__						:	List[str] =				field
          a__						:	List[Any] =				path_or_paths if isinstance(lowercase					,  lowercase) else {self.split: path_or_paths}
          a__						:	Dict =				Json(
              cache_dir=lowercase					,  data_files=lowercase					,  features=lowercase					,  field=lowercase					,  **lowercase					,  )

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

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

                 self.builder.download_and_prepare(
                     download_config=lowercase					,  download_mode=lowercase					,  verification_mode=lowercase					,  base_path=lowercase					,  num_proc=self.num_proc					,  )
                 a__						:	str =				self.builder.as_dataset(
                     split=self.split					,  verification_mode=lowercase					,  in_memory=self.keep_in_memory)
          return dataset





class  A__    :




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase					,  lowercase = None					,  lowercase = None					,  **lowercase					,  )			->						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.')

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

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

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

   def 							__lowercase		(					self					,  lowercase)			->						Tuple:







          '''simple docstring'''

          a__ ,							a__ ,							a__ ,							a__ ,							a__						:	Optional[int] =				args

          a__						:	Tuple =				query_table(
              table=self.dataset.data					,  key=slice(lowercase					,  offset + self.batch_size)					,  indices=self.dataset._indices					,  )
          a__						:	List[Any] =				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 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  **lowercase					,  )			->						int:







          '''simple docstring'''

          a__						:	Optional[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'					,  ):
                        a__						:	Any =				self._batch_json((offset, orient, lines, index, to_json_kwargs))
                        written += file_obj.write(lowercase)
          else:
                 a__ ,							a__						:	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					,  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









from collections.abc import Iterable
from typing import Generic, TypeVar

lowercase      :       Any									=  TypeVar("""_T""")





class  A__    (							Generic[_T]	):




   """simple docstring"""



   def __init__(					self					,  lowercase = None)			->						None:







          '''simple docstring'''

          a__						:	list[_T] =				list(iterable or [])
          a__						:	list[_T] =				[]

   def __len__(					self)			->						int:







          '''simple docstring'''

          return len(self._stacka) + len(self._stacka)

   def __repr__(					self)			->						str:







          '''simple docstring'''

          return F'Queue({tuple(self._stacka[::-1] + self._stacka)})'

   def 							__lowercase		(					self					,  lowercase)			->						None:







          '''simple docstring'''

          self._stacka.append(lowercase)

   def 							__lowercase		(					self)			->						_T:







          '''simple docstring'''

          a__						:	List[str] =				self._stacka.pop
          a__						:	Optional[int] =				self._stacka.append

          if not self._stacka:
                 while self._stacka:
                        stacka_append(stacka_pop())

          if not self._stacka:
                 raise IndexError('Queue is empty')
          return self._stacka.pop()


if __name__ == "__main__":
    from doctest import testmod

    testmod()


