





"""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   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


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


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

              self.builder.download_and_prepare(
                  download_config=__a       , download_mode=__a       , verification_mode=__a       , base_path=__a       , num_proc=self.num_proc       , )
              _UpperCamelCase   :						Optional[int]    =							self.builder.as_dataset(
                  split=self.split       , verification_mode=__a       , in_memory=self.keep_in_memory						)
        return dataset





class   __SCREAMING_SNAKE_CASE     :







  '''simple docstring'''


  def __init__(		self :							Dict       , __a :							Dataset       , __a :							Union[PathLike, BinaryIO]       , __a :							Optional[int] = None       , __a :							Optional[int] = None       , **__a :							List[Any]       , )		->					List[str]:
        if num_proc is not None and num_proc <= 0:
              raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''						)

        _UpperCamelCase   :						str    =							dataset
        _UpperCamelCase   :						Union[str, Any]    =							path_or_buf
        _UpperCamelCase   :						Optional[Any]    =							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        _UpperCamelCase   :						Optional[Any]    =							num_proc
        _UpperCamelCase   :						str    =							"utf-8"
        _UpperCamelCase   :						Optional[int]    =							to_json_kwargs

  def  __SCREAMING_SNAKE_CASE					(		self :							Any						)		->					int:
        _UpperCamelCase   :						str    =							self.to_json_kwargs.pop("path_or_buf"       , __a						)
        _UpperCamelCase   :						Optional[int]    =							self.to_json_kwargs.pop("orient"       , "records"						)
        _UpperCamelCase   :						Dict    =							self.to_json_kwargs.pop("lines"       , True if orient == "records" else False						)
        _UpperCamelCase   :						Tuple    =							self.to_json_kwargs.pop("index"       , False if orient in ["split", "table"] else True						)
        _UpperCamelCase   :						str    =							self.to_json_kwargs.pop("compression"       , __a						)

        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=__a						) as buffer:
                    _UpperCamelCase   :						str    =							self._write(file_obj=__a       , orient=__a       , lines=__a       , index=__a       , **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   :						Dict    =							self._write(
                  file_obj=self.path_or_buf       , orient=__a       , lines=__a       , index=__a       , **self.to_json_kwargs						)
        return written

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , __a :							Optional[int]						)		->					str:
        _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						List[str]    =							args

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


  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]       , __a :							BinaryIO       , __a :							Any       , __a :							Tuple       , __a :							Any       , **__a :							Tuple       , )		->					int:
        _UpperCamelCase   :						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"       , ):
                    _UpperCamelCase   :						Any    =							self._batch_json((offset, orient, lines, index, to_json_kwargs)						)
                    written += file_obj.write(__a						)
        else:
              _UpperCamelCase,	_UpperCamelCase   :						int    =							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       , __a       , __a						)]       , )       , 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(__a						)

        return written







"""simple docstring"""


import random
from typing import Any




def        lowercase__     (       lowercase_      )       ->		list[Any]:




      """simple docstring"""

      for _ in range(len(lowercase_      )      ):
            _UpperCamelCase   :						Dict    =							random.randint(0		,len(lowercase_      ) - 1      )
            _UpperCamelCase   :						str    =							random.randint(0		,len(lowercase_      ) - 1      )
            _UpperCamelCase,	_UpperCamelCase   :						Optional[Any]    =							data[b], data[a]
      return data


if __name__ == "__main__":
  lowerCamelCase__						= [0, 1, 2, 3, 4, 5, 6, 7]
  lowerCamelCase__						= ["python", "says", "hello", "!"]
  print("Fisher-Yates Shuffle:")
  print("List", integers, strings)
  print("FY Shuffle", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
