





def lowerCAmelCase__(__snake_case							)       ->   List[Any]:







	'''simple docstring'''







	stooge(__snake_case ,0 ,len(__snake_case							) - 1							)
	return arr





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case							)       ->   Optional[Any]:







	'''simple docstring'''







	if i >= h:
		return

	# If first element is smaller than the last then swap them
	if arr[i] > arr[h]:
		lowerCamelCase__       ,			lowerCamelCase__              =	arr[h], arr[i]

	# If there are more than 2 elements in the array
	if h - i + 1 > 2:
		lowerCamelCase__              =	(int)((h - i + 1) / 3							)

		# Recursively sort first 2/3 elements
		stooge(__snake_case ,__snake_case ,(h - t)							)

		# Recursively sort last 2/3 elements
		stooge(__snake_case ,i + t ,(__snake_case)							)

		# Recursively sort first 2/3 elements
		stooge(__snake_case ,__snake_case ,(h - t)							)


if __name__ == "__main__":
			_a						=   input("Enter numbers separated by a comma:\n").strip()
			_a						=   [int(item) for item in user_input.split(",")]
			print(stooge_sort(unsorted))










import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


_a						=   logging.get_logger(__name__)






@dataclass
class       __A					:

			'''simple docstring'''







			lowerCAmelCase_							  =		field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()				)}				)
			lowerCAmelCase_							  =		field(
			    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}				)
			lowerCAmelCase_							  =		field(
			    default=128    ,       metadata={
			        """help""": (
			            """The maximum total input sequence length after tokenization. Sequences longer """
			            """than this will be truncated, sequences shorter will be padded."""
			        )
			    }    ,       )
			lowerCAmelCase_							  =		field(
			    default=lowerCAmelCase    ,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""}				)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.task_name.lower()






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		"""train"""
			lowerCAmelCase_							  =		"""dev"""
			lowerCAmelCase_							  =		"""test"""






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		42
			lowerCAmelCase_							  =		42
			lowerCAmelCase_							  =		42





			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase = None ,						__lowerCAmelCase = Split.train ,						__lowerCAmelCase = None ,						):



				'''simple docstring'''







				warnings.warn(
				    '''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
				    '''library. You can have a look at this example script for pointers: '''
				    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py''' ,						__lowerCAmelCase ,						)
				lowerCamelCase__              =	args
				lowerCamelCase__              =	glue_processors[args.task_name]()
				lowerCamelCase__              =	glue_output_modes[args.task_name]
				if isinstance(__lowerCAmelCase ,						__lowerCAmelCase							):
					try:
						lowerCamelCase__              =	Split[mode]
					except KeyError:
						raise KeyError('''mode is not a valid split name'''							)
        # Load data features from cache or dataset file
				lowerCamelCase__              =	os.path.join(
				    cache_dir if cache_dir is not None else args.data_dir ,						F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}' ,						)
				lowerCamelCase__              =	self.processor.get_labels()
				if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
				    "RobertaTokenizer",
				    "RobertaTokenizerFast",
				    "XLMRobertaTokenizer",
				    "BartTokenizer",
				    "BartTokenizerFast",
				):
					# HACK(label indices are swapped in RoBERTa pretrained model)
					lowerCamelCase__       ,			lowerCamelCase__              =	label_list[2], label_list[1]
				lowerCamelCase__              =	label_list

				# Make sure only the first process in distributed training processes the dataset,
				# and the others will use the cache.
				lowerCamelCase__              =	cached_features_file + '''.lock'''
				with FileLock(__lowerCAmelCase							):
					if os.path.exists(__lowerCAmelCase							) and not args.overwrite_cache:
						lowerCamelCase__              =	time.time()
						lowerCamelCase__              =	torch.load(__lowerCAmelCase							)
						logger.info(
						    F'Loading features from cached file {cached_features_file} [took %.3f s]' ,						time.time() - start							)
					else:
						logger.info(F'Creating features from dataset file at {args.data_dir}'							)

						if mode == Split.dev:
							lowerCamelCase__              =	self.processor.get_dev_examples(args.data_dir							)
						elif mode == Split.test:
							lowerCamelCase__              =	self.processor.get_test_examples(args.data_dir							)
						else:
							lowerCamelCase__              =	self.processor.get_train_examples(args.data_dir							)
						if limit_length is not None:
							lowerCamelCase__              =	examples[:limit_length]
						lowerCamelCase__              =	glue_convert_examples_to_features(
						    __lowerCAmelCase ,						__lowerCAmelCase ,						max_length=args.max_seq_length ,						label_list=__lowerCAmelCase ,						output_mode=self.output_mode ,						)
						lowerCamelCase__              =	time.time()
						torch.save(self.features ,						__lowerCAmelCase							)
						# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
						logger.info(
						    F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'							)





			def __len__(					self							):



				'''simple docstring'''







				return len(self.features							)





			def __getitem__(					self ,						__lowerCAmelCase							):



				'''simple docstring'''







				return self.features[i]





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				return self.label_list



