

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    (					lowercase						):




	def __init__(		self	:						Union[str, Any]		,	lowerCAmelCase	:						NestedDataStructureLike[PathLike]		,	lowerCAmelCase	:						Optional[NamedSplit] = None		,	lowerCAmelCase	:						Optional[Features] = None		,	lowerCAmelCase	:						str = None		,	lowerCAmelCase	:						bool = False		,	lowerCAmelCase	:						bool = False		,	lowerCAmelCase	:						Optional[str] = None		,	lowerCAmelCase	:						Optional[int] = None		,	**lowerCAmelCase	:						Dict		,	):


		'''simple docstring'''







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




	def       __A					(		self	:						Any							):


		'''simple docstring'''







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

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


class 						__UpperCamelCase    :




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

		UpperCAmelCase_       				=			dataset
		UpperCAmelCase_       				=			path_or_buf
		UpperCAmelCase_       				=			batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
		UpperCAmelCase_       				=			num_proc
		UpperCAmelCase_       				=			"utf-8"
		UpperCAmelCase_       				=			to_json_kwargs




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.to_json_kwargs.pop("path_or_buf"		,	lowerCAmelCase							)
		UpperCAmelCase_       				=			self.to_json_kwargs.pop("orient"		,	"records"							)
		UpperCAmelCase_       				=			self.to_json_kwargs.pop("lines"		,	True if orient == "records" else False							)
		UpperCAmelCase_       				=			self.to_json_kwargs.pop("index"		,	False if orient in ["split", "table"] else True							)
		UpperCAmelCase_       				=			self.to_json_kwargs.pop("compression"		,	lowerCAmelCase							)

		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=lowerCAmelCase							) as buffer:
				UpperCAmelCase_       				=			self._write(file_obj=lowerCAmelCase		,	orient=lowerCAmelCase		,	lines=lowerCAmelCase		,	index=lowerCAmelCase		,	**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_       				=			self._write(
			    file_obj=self.path_or_buf		,	orient=lowerCAmelCase		,	lines=lowerCAmelCase		,	index=lowerCAmelCase		,	**self.to_json_kwargs							)
		return written




	def       __A					(		self	:						Optional[Any]		,	lowerCAmelCase	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			args

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




	def       __A					(		self	:						Optional[int]		,	lowerCAmelCase	:						BinaryIO		,	lowerCAmelCase	:						Optional[int]		,	lowerCAmelCase	:						Optional[int]		,	lowerCAmelCase	:						Union[str, Any]		,	**lowerCAmelCase	:						Optional[int]		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			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_       				=			self._batch_json((offset, orient, lines, index, to_json_kwargs)							)
				written += file_obj.write(lowerCAmelCase							)
		else:
			UpperCAmelCase_				,   UpperCAmelCase_       				=			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		,	lowerCAmelCase		,	lowerCAmelCase							)]		,	)		,	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(lowerCAmelCase							)

		return written


from __future__ import annotations

from collections import Counter
from random import random

class 						__UpperCamelCase    :




	def __init__(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{}




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{}




	def       __A					(		self	:						Union[str, Any]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						float							):


		'''simple docstring'''







		if nodea not in self.connections:
			self.add_node(lowerCAmelCase							)
		if nodea not in self.connections:
			self.add_node(lowerCAmelCase							)
		UpperCAmelCase_       				=			probability




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		return list(self.connections							)




	def       __A					(		self	:						str		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			0
		UpperCAmelCase_       				=			random()

		for dest in self.connections[node]:
			current_probability += self.connections[node][dest]
			if current_probability > random_value:
				return dest
		return ""



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			MarkovChainGraphUndirectedUnweighted()

	for nodea, nodea, probability in transitions:
		graph.add_transition_probability(A       ,     A       ,     A    )

	UpperCAmelCase_       				=			Counter(graph.get_nodes()    )
	UpperCAmelCase_       				=			start

	for _ in range(A    ):
		UpperCAmelCase_       				=			graph.transition(A    )
		visited[node] += 1

	return visited


if __name__ == "__main__":
				import doctest

				doctest.testmod()