






'''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						(		snake_case			):




    def __init__(			self  :       Tuple  ,     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[Any]  ,     ):






       '''simple docstring'''






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





    def 				lowerCAmelCase__				(			self  :       Optional[int]			):






       '''simple docstring'''






       if self.streaming:
          lowercase_	:    Any           =     self.builder.as_streaming_dataset(split=self.split			)
       # Build regular (map-style) dataset
       else:
          lowercase_	:    Dict           =     None
          lowercase_	:    Optional[int]           =     None
          lowercase_	:    str           =     None
          lowercase_	:    str           =     None

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


class        _UpperCAmelCase						:




    def __init__(			self  :       str  ,     a  :       Dataset  ,     a  :       Union[PathLike, BinaryIO]  ,     a  :       Optional[int] = None  ,     a  :       Optional[int] = None  ,     **a  :       List[Any]  ,     ):






       '''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_	:    Dict           =     dataset
       lowercase_	:    Optional[int]           =     path_or_buf
       lowercase_	:    List[Any]           =     batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
       lowercase_	:    Optional[Any]           =     num_proc
       lowercase_	:    List[Any]           =     "utf-8"
       lowercase_	:    List[str]           =     to_json_kwargs




    def 				lowerCAmelCase__				(			self  :       Optional[int]			):






       '''simple docstring'''






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




    def 				lowerCAmelCase__				(			self  :       Optional[int]  ,     a  :       List[str]			):






       '''simple docstring'''






       lowercase_    ,		lowercase_    ,		lowercase_    ,		lowercase_    ,		lowercase_	:    List[Any]           =     args

       lowercase_	:    Optional[int]           =     query_table(
           table=self.dataset.data  ,     key=slice(a  ,     offset + self.batch_size			)  ,     indices=self.dataset._indices  ,     )
       lowercase_	:    Dict           =     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 				lowerCAmelCase__				(			self  :       int  ,     a  :       BinaryIO  ,     a  :       int  ,     a  :       str  ,     a  :       Union[str, Any]  ,     **a  :       str  ,     ):






       '''simple docstring'''






       lowercase_	:    Union[str, 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"  ,     ):
             lowercase_	:    Dict           =     self._batch_json((offset, orient, lines, index, to_json_kwargs)			)
             written += file_obj.write(a			)
       else:
          lowercase_    ,		lowercase_	:    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  ,     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 os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


UpperCamelCase__ =			namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   return (abs(source - target							) / target) < 0.01





@pytest.mark.integration
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Optional[int]           =     _TestCommandArgs(dataset=_UpperCamelCase						,		all_configs=_UpperCamelCase						,		save_infos=_UpperCamelCase							)
   lowercase_	:    int           =     TestCommand(*_UpperCamelCase							)
   test_command.run()
   lowercase_	:    List[str]           =     os.path.join(_UpperCamelCase						,		"README.md"							)
   assert os.path.exists(_UpperCamelCase							)
   lowercase_	:    Any           =     DatasetInfosDict.from_directory(_UpperCamelCase							)
   lowercase_	:    Optional[int]           =     DatasetInfosDict(
       {
           "default": DatasetInfo(
               features=Features(
                   {
                       "tokens": Sequence(Value("string"							)							),
                       "ner_tags": Sequence(
                           ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]							)							),
                       "langs": Sequence(Value("string"							)							),
                       "spans": Sequence(Value("string"							)							),
                   }							)						,		splits=[
                   {
                       "name": "train",
                       "num_bytes": 235_1563,
                       "num_examples": 1_0000,
                   },
                   {
                       "name": "validation",
                       "num_bytes": 23_8418,
                       "num_examples": 1000,
                   },
               ]						,		download_size=394_0680						,		dataset_size=258_9981						,		)
       }							)
   assert dataset_infos.keys() == expected_dataset_infos.keys()
   for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
      lowercase_    ,		lowercase_	:    Optional[int]           =     getattr(dataset_infos["default"]						,		_UpperCamelCase							), getattr(expected_dataset_infos["default"]						,		_UpperCamelCase							)
      if key == "num_bytes":
         assert is_apercent_close(_UpperCamelCase						,		_UpperCamelCase							)
      elif key == "splits":
         assert list(_UpperCamelCase							) == list(_UpperCamelCase							)
         for split in result:
            assert result[split].name == expected[split].name
            assert result[split].num_examples == expected[split].num_examples
            assert is_apercent_close(result[split].num_bytes						,		expected[split].num_bytes							)
      else:
         result == expected




