

import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors





@require_tokenizers
class      snake_case_      (					_a      ,			unittest.TestCase				):


							"""simple docstring"""





							__UpperCAmelCase      =BartTokenizer
							__UpperCAmelCase      =BartTokenizerFast
							__UpperCAmelCase      =True
							__UpperCAmelCase      =filter_roberta_detectors




							def 							A__	(			self				):
												super().setUp()
												__lowerCAmelCase       =				[
												    'l',
												    'o',
												    'w',
												    'e',
												    'r',
												    's',
												    't',
												    'i',
												    'd',
												    'n',
												    '\u0120',
												    '\u0120l',
												    '\u0120n',
												    '\u0120lo',
												    '\u0120low',
												    'er',
												    '\u0120lowest',
												    '\u0120newer',
												    '\u0120wider',
												    '<unk>',
												]
												__lowerCAmelCase       =				dict(zip(_A       ,    range(len(_A				)				)				)				)
												__lowerCAmelCase       =				['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
												__lowerCAmelCase       =				{'unk_token': '<unk>'}

												__lowerCAmelCase       =				os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES['vocab_file']				)
												__lowerCAmelCase       =				os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES['merges_file']				)
												with open(self.vocab_file       ,    'w'       ,    encoding='utf-8'				) as fp:
																	fp.write(json.dumps(_A				) + '\n'				)
												with open(self.merges_file       ,    'w'       ,    encoding='utf-8'				) as fp:
																	fp.write('\n'.join(_A				)				)




							def 							A__	(			self       ,    **_A				):
												kwargs.update(self.special_tokens_map				)
												return self.tokenizer_class.from_pretrained(self.tmpdirname       ,    **_A				)




							def 							A__	(			self       ,    **_A				):
												kwargs.update(self.special_tokens_map				)
												return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,    **_A				)




							def 							A__	(			self       ,    _A				):
												return "lower newer", "lower newer"




							@cached_property
							def 							A__	(			self				):
												return BartTokenizer.from_pretrained('facebook/bart-large'				)




							@cached_property
							def 							A__	(			self				):
												return BartTokenizerFast.from_pretrained('facebook/bart-large'				)




							@require_torch
							def 							A__	(			self				):
												__lowerCAmelCase       =				['A long paragraph for summarization.', 'Another paragraph for summarization.']
												__lowerCAmelCase       =				[0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	__lowerCAmelCase       =				tokenizer(_A       ,    max_length=len(_A				)       ,    padding=_A       ,    return_tensors='pt'				)
																	self.assertIsInstance(_A       ,    _A				)

																	self.assertEqual((2, 9)       ,    batch.input_ids.shape				)
																	self.assertEqual((2, 9)       ,    batch.attention_mask.shape				)
																	__lowerCAmelCase       =				batch.input_ids.tolist()[0]
																	self.assertListEqual(_A       ,    _A				)
																	# Test that special tokens are reset




							@require_torch
							def 							A__	(			self				):
												__lowerCAmelCase       =				['A long paragraph for summarization.', 'Another paragraph for summarization.']
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	__lowerCAmelCase       =				tokenizer(_A       ,    padding=_A       ,    return_tensors='pt'				)
																	# check if input_ids are returned and no labels
																	self.assertIn('input_ids'       ,    _A				)
																	self.assertIn('attention_mask'       ,    _A				)
																	self.assertNotIn('labels'       ,    _A				)
																	self.assertNotIn('decoder_attention_mask'       ,    _A				)




							@require_torch
							def 							A__	(			self				):
												__lowerCAmelCase       =				[
												    'Summary of the text.',
												    'Another summary.',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	__lowerCAmelCase       =				tokenizer(text_target=_A       ,    max_length=3_2       ,    padding='max_length'       ,    return_tensors='pt'				)
																	self.assertEqual(3_2       ,    targets['input_ids'].shape[1]				)




							@require_torch
							def 							A__	(			self				):
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	__lowerCAmelCase       =				tokenizer(
																	    ['I am a small frog' * 1_0_2_4, 'I am a small frog']       ,    padding=_A       ,    truncation=_A       ,    return_tensors='pt'				)
																	self.assertIsInstance(_A       ,    _A				)
																	self.assertEqual(batch.input_ids.shape       ,    (2, 1_0_2_4)				)




							@require_torch
							def 							A__	(			self				):
												__lowerCAmelCase       =				['A long paragraph for summarization.']
												__lowerCAmelCase       =				[
												    'Summary of the text.',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	__lowerCAmelCase       =				tokenizer(_A       ,    return_tensors='pt'				)
																	__lowerCAmelCase       =				tokenizer(text_target=_A       ,    return_tensors='pt'				)
																	__lowerCAmelCase       =				inputs['input_ids']
																	__lowerCAmelCase       =				targets['input_ids']
																	self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()				)
																	self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()				)
																	self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()				)
																	self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()				)




							def 							A__	(			self				):
												pass




							def 							A__	(			self				):
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
																						__lowerCAmelCase       =				self.rust_tokenizer_class.from_pretrained(_A       ,    **_A				)
																						__lowerCAmelCase       =				self.tokenizer_class.from_pretrained(_A       ,    **_A				)
																						__lowerCAmelCase       =				'A, <mask> AllenNLP sentence.'
																						__lowerCAmelCase       =				tokenizer_r.encode_plus(_A       ,    add_special_tokens=_A       ,    return_token_type_ids=_A				)
																						__lowerCAmelCase       =				tokenizer_p.encode_plus(_A       ,    add_special_tokens=_A       ,    return_token_type_ids=_A				)

																						# token_type_ids should put 0 everywhere
																						self.assertEqual(sum(tokens_r['token_type_ids']				)       ,    sum(tokens_p['token_type_ids']				)				)

																						# attention_mask should put 1 everywhere, so sum over length should be 1
																						self.assertEqual(
																						    sum(tokens_r['attention_mask']				) / len(tokens_r['attention_mask']				)       ,    sum(tokens_p['attention_mask']				) / len(tokens_p['attention_mask']				)       ,    )

																						__lowerCAmelCase       =				tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']				)
																						__lowerCAmelCase       =				tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']				)

																						# Rust correctly handles the space before the mask while python doesnt
																						self.assertSequenceEqual(tokens_p['input_ids']       ,    [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]				)
																						self.assertSequenceEqual(tokens_r['input_ids']       ,    [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]				)

																						self.assertSequenceEqual(
																						    _A       ,    ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']				)
																						self.assertSequenceEqual(
																						    _A       ,    ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']				)









import random


def 							__lowercase     (	UpperCAmelCase__   ):

					"""simple docstring"""






					def is_sorted(UpperCAmelCase__   ):
										for i in range(len(UpperCAmelCase__   ) - 1   ):
															if collection[i] > collection[i + 1]:
																				return False
										return True

					while not is_sorted(UpperCAmelCase__   ):
										random.shuffle(UpperCAmelCase__   )
					return collection


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






