



__snake_case						:Dict						=					[
    "VerificationMode",
    "Version",
    "disable_progress_bar",
    "enable_progress_bar",
    "is_progress_bar_enabled",
    "experimental",
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental










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							:					Dict					,		__SCREAMING_SNAKE_CASE							:					NestedDataStructureLike[PathLike]					,		__SCREAMING_SNAKE_CASE							:					Optional[NamedSplit] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[Features] = None					,		__SCREAMING_SNAKE_CASE							:					str = None					,		__SCREAMING_SNAKE_CASE							:					bool = False					,		__SCREAMING_SNAKE_CASE							:					bool = False					,		__SCREAMING_SNAKE_CASE							:					Optional[str] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		**__SCREAMING_SNAKE_CASE							:					Any					,		):


           '''simple docstring'''

           super().__init__(
               __SCREAMING_SNAKE_CASE					,		split=__SCREAMING_SNAKE_CASE					,		features=__SCREAMING_SNAKE_CASE					,		cache_dir=__SCREAMING_SNAKE_CASE					,		keep_in_memory=__SCREAMING_SNAKE_CASE					,		streaming=__SCREAMING_SNAKE_CASE					,		num_proc=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)
           __a							=    field
           __a							=    path_or_paths if isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE) else {self.split: path_or_paths}
           __a							=    Json(
               cache_dir=__SCREAMING_SNAKE_CASE					,		data_files=__SCREAMING_SNAKE_CASE					,		features=__SCREAMING_SNAKE_CASE					,		field=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)





       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           if self.streaming:
               __a							=    self.builder.as_streaming_dataset(split=self.split)
           # Build regular (map-style) dataset
           else:
               __a							=    None
               __a							=    None
               __a							=    None
               __a							=    None

               self.builder.download_and_prepare(
                   download_config=__SCREAMING_SNAKE_CASE					,		download_mode=__SCREAMING_SNAKE_CASE					,		verification_mode=__SCREAMING_SNAKE_CASE					,		base_path=__SCREAMING_SNAKE_CASE					,		num_proc=self.num_proc					,		)
               __a							=    self.builder.as_dataset(
                   split=self.split					,		verification_mode=__SCREAMING_SNAKE_CASE					,		in_memory=self.keep_in_memory)
           return dataset


class    _A	:



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


           '''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							=    dataset
           __a							=    path_or_buf
           __a							=    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
           __a							=    num_proc
           __a							=    '''utf-8'''
           __a							=    to_json_kwargs



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           __a							=    self.to_json_kwargs.pop('''path_or_buf'''					,		__SCREAMING_SNAKE_CASE)
           __a							=    self.to_json_kwargs.pop('''orient'''					,		'''records''')
           __a							=    self.to_json_kwargs.pop('''lines'''					,		True if orient == '''records''' else False)
           __a							=    self.to_json_kwargs.pop('''index'''					,		False if orient in ['''split''', '''table'''] else True)
           __a							=    self.to_json_kwargs.pop('''compression'''					,		__SCREAMING_SNAKE_CASE)

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



       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           __a	,       __a	,       __a	,       __a	,       __a							=    args

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





       def 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					BinaryIO					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		**__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		):


           '''simple docstring'''

           __a							=    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							=    self._batch_json((offset, orient, lines, index, to_json_kwargs))
                   written += file_obj.write(__SCREAMING_SNAKE_CASE)
           else:
               __a	,       __a							=    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					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)]					,		)					,		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(__SCREAMING_SNAKE_CASE)

           return written





