
import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = False  ,						SCREAMING_SNAKE_CASE_ = False  ,						SCREAMING_SNAKE_CASE_ = None  ,						**SCREAMING_SNAKE_CASE_  ,						)-> Optional[int]:





													'''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_  ,						)
													__UpperCamelCase  	=      path_or_paths if isinstance(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ) else {self.split: path_or_paths}
													__UpperCamelCase  	=      Csv(
													    cache_dir=SCREAMING_SNAKE_CASE_  ,						data_files=SCREAMING_SNAKE_CASE_  ,						features=SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_  ,						)



							def      A__		(	self      )-> List[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=SCREAMING_SNAKE_CASE_  ,						download_mode=SCREAMING_SNAKE_CASE_  ,						verification_mode=SCREAMING_SNAKE_CASE_  ,						base_path=SCREAMING_SNAKE_CASE_  ,						num_proc=self.num_proc  ,						)
																			__UpperCamelCase  	=      self.builder.as_dataset(
																			    split=self.split  ,						verification_mode=SCREAMING_SNAKE_CASE_  ,						in_memory=self.keep_in_memory      )
													return dataset






class 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = None  ,						**SCREAMING_SNAKE_CASE_  ,						)-> 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."      )

													__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_csv_kwargs



							def      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase  	=      self.to_csv_kwargs.pop('''path_or_buf'''  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      self.to_csv_kwargs.pop('''header'''  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      self.to_csv_kwargs.pop('''index'''  ,						SCREAMING_SNAKE_CASE_      )

													if isinstance(self.path_or_buf  ,						(str, bytes, os.PathLike)      ):
																			with open(self.path_or_buf  ,						'''wb+'''      ) as buffer:
																									__UpperCamelCase  	=      self._write(file_obj=SCREAMING_SNAKE_CASE_  ,						header=SCREAMING_SNAKE_CASE_  ,						index=SCREAMING_SNAKE_CASE_  ,						**self.to_csv_kwargs      )
													else:
																			__UpperCamelCase  	=      self._write(file_obj=self.path_or_buf  ,						header=SCREAMING_SNAKE_CASE_  ,						index=SCREAMING_SNAKE_CASE_  ,						**self.to_csv_kwargs      )
													return written



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> int:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase  	=      args

													__UpperCamelCase  	=      query_table(
													    table=self.dataset.data  ,						key=slice(SCREAMING_SNAKE_CASE_  ,						offset + self.batch_size      )  ,						indices=self.dataset._indices  ,						)
													__UpperCamelCase  	=      batch.to_pandas().to_csv(
													    path_or_buf=SCREAMING_SNAKE_CASE_  ,						header=header if (offset == 0) else False  ,						index=SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )
													return csv_str.encode(self.encoding      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )-> 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 CSV from Arrow format'''  ,						):
																									__UpperCamelCase  	=      self._batch_csv((offset, header, index, to_csv_kwargs)      )
																									written += file_obj.write(SCREAMING_SNAKE_CASE_      )

													else:
																			__UpperCamelCase	,				__UpperCamelCase  	=      len(self.dataset      ), self.batch_size
																			with multiprocessing.Pool(self.num_proc      ) as pool:
																									for csv_str in logging.tqdm(
																									    pool.imap(
																									        self._batch_csv  ,						[(offset, header, index, to_csv_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 CSV from Arrow format'''  ,						):
																															written += file_obj.write(SCREAMING_SNAKE_CASE_      )

													return written












from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar

class        _SCREAMING_SNAKE_CASE			(     _a ):





	@abstractmethod
	def __lt__(						self							:   T					,    __lowerCamelCase							:   T      ):
		pass





	@abstractmethod
	def __gt__(						self							:   T					,    __lowerCamelCase							:   T      ):
		pass





	@abstractmethod
	def __eq__(						self							:   T					,    __lowerCamelCase							:   object      ):
		pass
UpperCAmelCase_					:		str           = TypeVar('''T''', bound=Comparable)

class        _SCREAMING_SNAKE_CASE			(     Generic[T] ):





	def __init__(						self							:   Dict      ):
		UpperCamelCase	:list[T]		=      []
		UpperCamelCase	:int		=      0





	def __repr__(						self							:   List[str]      ):
		return str(self.h      )





	def 		_A					(						self							:   Tuple					,    __lowerCamelCase							:   int      ):
		if child_idx > 0:
			return (child_idx - 1) // 2
		return None





	def 		_A					(						self							:   List[str]					,    __lowerCamelCase							:   int      ):
		UpperCamelCase	:Optional[int]		=      2 * parent_idx + 1
		if left_child_index < self.heap_size:
			return left_child_index
		return None





	def 		_A					(						self							:   List[str]					,    __lowerCamelCase							:   int      ):
		UpperCamelCase	:Optional[Any]		=      2 * parent_idx + 2
		if right_child_index < self.heap_size:
			return right_child_index
		return None





	def 		_A					(						self							:   Tuple					,    __lowerCamelCase							:   int      ):
		if index < self.heap_size:
			UpperCamelCase	:int		=      index
			UpperCamelCase	:Dict		=      self.left_child_idx(__lowerCamelCase      )
			UpperCamelCase	:Any		=      self.right_child_idx(__lowerCamelCase      )
			# check which child is larger than its parent
			if left_child is not None and self.h[left_child] > self.h[violation]:
				UpperCamelCase	:Dict		=      left_child
			if right_child is not None and self.h[right_child] > self.h[violation]:
				UpperCamelCase	:Dict		=      right_child
			# if violation indeed exists
			if violation != index:
				# swap to fix the violation
				UpperCamelCase	, UpperCamelCase	:Optional[int]		=      self.h[index], self.h[violation]
				# fix the subsequent violation recursively if any
				self.max_heapify(__lowerCamelCase      )





	def 		_A					(						self							:   Any					,    __lowerCamelCase							:   Iterable[T]      ):
		UpperCamelCase	:Optional[Any]		=      list(__lowerCamelCase      )
		UpperCamelCase	:int		=      len(self.h      )
		if self.heap_size > 1:
			# max_heapify from right to left but exclude leaves (last level)
			for i in range(self.heap_size // 2 - 1					,    -1					,    -1      ):
				self.max_heapify(__lowerCamelCase      )





	def 		_A					(						self							:   Optional[int]      ):
		if self.heap_size >= 2:
			UpperCamelCase	:List[str]		=      self.h[0]
			UpperCamelCase	:str		=      self.h.pop(-1      )
			self.heap_size -= 1
			self.max_heapify(0      )
			return me
		elif self.heap_size == 1:
			self.heap_size -= 1
			return self.h.pop(-1      )
		else:
			raise Exception("""Empty heap"""      )





	def 		_A					(						self							:   Optional[Any]					,    __lowerCamelCase							:   T      ):
		self.h.append(__lowerCamelCase      )
		UpperCamelCase	:Any		=      (self.heap_size - 1) // 2
		self.heap_size += 1
		while idx >= 0:
			self.max_heapify(__lowerCamelCase      )
			UpperCamelCase	:List[str]		=      (idx - 1) // 2





	def 		_A					(						self							:   int      ):
		UpperCamelCase	:int		=      self.heap_size
		for j in range(size - 1					,    0					,    -1      ):
			UpperCamelCase	, UpperCamelCase	:Any		=      self.h[j], self.h[0]
			self.heap_size -= 1
			self.max_heapify(0      )
		UpperCamelCase	:List[str]		=      size


if __name__ == "__main__":
	import doctest

	# run doc test
	doctest.testmod()

	# demo
	for unsorted in [
	    [0],
	    [2],
	    [3, 5],
	    [5, 3],
	    [5, 5],
	    [0, 0, 0, 0],
	    [1, 1, 1, 1],
	    [2, 2, 3, 5],
	    [0, 2, 2, 3, 5],
	    [2, 5, 3, 0, 2, 3, 0, 3],
	    [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
	    [1_03, 9, 1, 7, 11, 15, 25, 2_01, 2_09, 1_07, 5],
	    [-45, -2, -5],
	]:
		print(F'''unsorted array: {unsorted}''')

		UpperCAmelCase_					:		Heap[int]           = Heap()
		heap.build_max_heap(unsorted)
		print(F'''after build heap: {heap}''')

		print(F'''max value: {heap.extract_max()}''')
		print(F'''after max value removed: {heap}''')

		heap.insert(1_00)
		print(F'''after new value 100 inserted: {heap}''')

		heap.heap_sort()
		print(F'''heap-sorted array: {heap}\n''')
