



from __future__ import annotations

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

snake_case    :						Optional[Any]  =						TypeVar('T')
snake_case    :						List[Any]  =						TypeVar('U')




class 							lowerCAmelCase__  (	Generic[T, U]						):







					def __init__(       self      :     Optional[Any]			,		_A      :     T | None			,		_A      :     U | None):
											A__	:							Union[str, Any]						    = key
											A__	:							Dict						    = val
											A__	:							DoubleLinkedListNode[T, U] | None						    = None
											A__	:							DoubleLinkedListNode[T, U] | None						    = None







					def __repr__(       self      :     Union[str, Any]):
											return (
											    F'Node: key: {self.key}, val: {self.val}, '
											    F'has next: {bool(self.next)}, has prev: {bool(self.prev)}'
											)




class 							lowerCAmelCase__  (	Generic[T, U]						):







					def __init__(       self      :     Union[str, Any]):
											A__	:							DoubleLinkedListNode[T, U]						    = DoubleLinkedListNode(_A			,		_A)
											A__	:							DoubleLinkedListNode[T, U]						    = DoubleLinkedListNode(_A			,		_A)
											A__    ,       A__	:							Tuple						    = self.rear, self.head







					def __repr__(       self      :     List[str]):
											A__	:							Union[str, Any]						    = ["DoubleLinkedList"]
											A__	:							int						    = self.head
											while node.next is not None:
																	rep.append(str(_A))
																	A__	:							str						    = node.next
											rep.append(str(self.rear))
											return ",\n    ".join(_A)







					def  _lowercase			(       self      :     List[Any]			,		_A      :     DoubleLinkedListNode[T, U]):
											A__	:							str						    = self.rear.prev

											# All nodes other than self.head are guaranteed to have non-None previous
											assert previous is not None

											A__	:							Dict						    = node
											A__	:							int						    = previous
											A__	:							Dict						    = node
											A__	:							List[Any]						    = self.rear







					def  _lowercase			(       self      :     int			,		_A      :     DoubleLinkedListNode[T, U]):

											if node.prev is None or node.next is None:
																	return None

											A__	:							List[str]						    = node.next
											A__	:							Union[str, Any]						    = node.prev
											A__	:							Optional[Any]						    = None
											A__	:							List[str]						    = None
											return node




class 							lowerCAmelCase__  (	Generic[T, U]						):
					__A     :    dict[Callable[[T], U], LRUCache[T, U]]           =						{}







					def __init__(       self      :     List[Any]			,		_A      :     int):
											A__	:							DoubleLinkedList[T, U]						    = DoubleLinkedList()
											A__	:							Union[str, Any]						    = capacity
											A__	:							List[str]						    = 0
											A__	:							Any						    = 0
											A__	:							int						    = 0
											A__	:							dict[T, DoubleLinkedListNode[T, U]]						    = {}







					def __repr__(       self      :     Optional[int]):
											return (
											    F'CacheInfo(hits={self.hits}, misses={self.miss}, '
											    F'capacity={self.capacity}, current size={self.num_keys})'
											)







					def __contains__(       self      :     str			,		_A      :     T):
											return key in self.cache







					def  _lowercase			(       self      :     Dict			,		_A      :     T):
											# Note: pythonic interface would throw KeyError rather than return None

											if key in self.cache:
																	self.hits += 1
																	A__	:							DoubleLinkedListNode[T, U]						    = self.cache[key]
																	A__	:							str						    = self.list.remove(self.cache[key])
																	assert node == value_node

																	# node is guaranteed not None because it is in self.cache
																	assert node is not None
																	self.list.add(_A)
																	return node.val
											self.miss += 1
											return None







					def  _lowercase			(       self      :     List[str]			,		_A      :     T			,		_A      :     U):

											if key not in self.cache:
																	if self.num_keys >= self.capacity:
																							# delete first node (oldest) when over capacity
																							A__	:							str						    = self.list.head.next

																							# guaranteed to have a non-None first node when num_keys > 0
																							# explain to type checker via assertions
																							assert first_node is not None
																							assert first_node.key is not None
																							assert (
																							    self.list.remove(_A) is not None
																							)  # node guaranteed to be in list assert node.key is not None

																							del self.cache[first_node.key]
																							self.num_keys -= 1
																	A__	:							Optional[int]						    = DoubleLinkedListNode(_A			,		_A)
																	self.list.add(self.cache[key])
																	self.num_keys += 1

											else:
																	# bump node to the end of the list, update value
																	A__	:							str						    = self.list.remove(self.cache[key])
																	assert node is not None  # node guaranteed to be in list
																	A__	:							int						    = value
																	self.list.add(_A)







					@classmethod
					def  _lowercase			(       cls      :     Dict			,		_A      :     int = 128):

											def cache_decorator_inner(_A      :     Callable[[T], U]) -> Callable[..., U]:
																	def cache_decorator_wrapper(*_A      :     T) -> U:
																							if func not in cls.decorator_function_to_instance_map:
																													A__	:							str						    = LRUCache(_A)

																							A__	:							Optional[int]						    = cls.decorator_function_to_instance_map[func].get(args[0])
																							if result is None:
																													A__	:							Tuple						    = func(*_A)
																													cls.decorator_function_to_instance_map[func].put(args[0]			,		_A)
																							return result

																	def cache_info() -> LRUCache[T, U]:
																							return cls.decorator_function_to_instance_map[func]

																	setattr(_A			,		"cache_info"			,		_A)  # noqa: B010

																	return cache_decorator_wrapper

											return cache_decorator_inner


if __name__ == "__main__":
		import doctest

		doctest.testmod()




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 							lowerCAmelCase__  (	UpperCamelCase						):







					def __init__(       self      :     Dict			,		_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      :     Dict			,		):
											super().__init__(
											    _A			,		split=_A			,		features=_A			,		cache_dir=_A			,		keep_in_memory=_A			,		streaming=_A			,		num_proc=_A			,		**_A			,		)
											A__	:							Optional[int]						    = field
											A__	:							Dict						    = path_or_paths if isinstance(_A			,		_A) else {self.split: path_or_paths}
											A__	:							int						    = Json(
											    cache_dir=_A			,		data_files=_A			,		features=_A			,		field=_A			,		**_A			,		)







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

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




class 							lowerCAmelCase__  :







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

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







					def  _lowercase			(       self      :     Union[str, Any]):
											A__	:							Any						    = self.to_json_kwargs.pop("path_or_buf"			,		_A)
											A__	:							Union[str, Any]						    = self.to_json_kwargs.pop("orient"			,		"records")
											A__	:							List[Any]						    = 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__	:							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__	:							int						    = 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__	:							Optional[Any]						    = self._write(
																	    file_obj=self.path_or_buf			,		orient=_A			,		lines=_A			,		index=_A			,		**self.to_json_kwargs)
											return written







					def  _lowercase			(       self      :     List[Any]			,		_A      :     int):
											A__    ,       A__    ,       A__    ,       A__    ,       A__	:							Union[str, Any]						    = args

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