

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		(__magic_name__       ):


					'''simple docstring'''






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






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

							self.builder.download_and_prepare(
							    download_config=A__					, download_mode=A__					, verification_mode=A__					, base_path=A__					, num_proc=self.num_proc					, )
							A__							:					Union[str, Any]       =	self.builder.as_dataset(
							    split=self.split					, verification_mode=A__					, in_memory=self.keep_in_memory    )
						return dataset



class 					_a		:


					'''simple docstring'''






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

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






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






					def 						__A    (				self					, A__    ):
						A__     ,     A__     ,     A__     ,     A__     ,     A__							:					int       =	args

						A__							:					Union[str, Any]       =	query_table(
						    table=self.dataset.data					, key=slice(A__					, offset + self.batch_size    )					, indices=self.dataset._indices					, )
						A__							:					List[Any]       =	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 						__A    (				self					, A__					, A__					, A__					, A__					, **A__					, ):
						A__							:					List[str]       =	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__							:					Optional[int]       =	self._batch_json((offset, orient, lines, index, to_json_kwargs)    )
								written += file_obj.write(A__    )
						else:
							A__     ,     A__							:					Optional[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






def 		UpperCamelCase      (lowercase_:				int      )  ->   int:
	if not isinstance(lowercase_					,   lowercase_      ):
		raise TypeError("""Input value must be an 'int' type"""      )

	A__							:					int       =	0
	while number:
		position += 1
		number >>= 1

	return position


if __name__ == "__main__":
	import doctest

	doctest.testmod()



