

import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin





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







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       str						      =    PreTrainedTokenizerFast
			SCREAMING_SNAKE_CASE					:       List[str]						      =    False
			SCREAMING_SNAKE_CASE					:       Tuple						      =    True
			SCREAMING_SNAKE_CASE					:       str						      =    "tokenizer_file"







			def     snake_case__(					self			:    Dict							)				->int:
									snake_case_       =     False  # because we don't have pretrained_vocab_files_map
									super().setUp()
									snake_case_       =     True

									snake_case_       =     ['''robot-test/dummy-tokenizer-fast''', '''robot-test/dummy-tokenizer-wordlevel''']
									snake_case_       =     '''SaulLu/dummy-tokenizer-bytelevel-bpe'''

									# Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
									snake_case_       =     [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

									snake_case_       =     PreTrainedTokenizerFast.from_pretrained(model_paths[0]							)
									tokenizer.save_pretrained(self.tmpdirname							)







			def     snake_case__(					self			:    Union[str, Any]							)				->Dict:
									# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
									# model
									pass







			def     snake_case__(					self			:    str							)				->Any:
									# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
									# model
									pass







			def     snake_case__(					self			:    List[Any]							)				->str:
									# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
									# model
									pass







			def     snake_case__(					self			:    str							)				->List[Any]:
									# PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
									pass







			def     snake_case__(					self			:    Any							)				->Union[str, Any]:
									snake_case_       =     self.tmpdirname
									# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
																					try:
																											snake_case_       =     tempfile.mkdtemp()
																											snake_case_       =     self.rust_tokenizer_class.from_pretrained(_UpperCamelCase							,       **_UpperCamelCase							)

																											tokenizer.save_pretrained(self.tmpdirname							)
																											super().test_training_new_tokenizer()
																					finally:
																											# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
																											# is restored
																											shutil.rmtree(self.tmpdirname							)
																											snake_case_       =     tmpdirname_orig







			def     snake_case__(					self			:    List[str]							)				->List[Any]:
									snake_case_       =     self.tmpdirname
									# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
																					try:
																											snake_case_       =     tempfile.mkdtemp()
																											snake_case_       =     self.rust_tokenizer_class.from_pretrained(_UpperCamelCase							,       **_UpperCamelCase							)

																											tokenizer.save_pretrained(self.tmpdirname							)
																											super().test_training_new_tokenizer_with_special_tokens_change()
																					finally:
																											# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
																											# is restored
																											shutil.rmtree(self.tmpdirname							)
																											snake_case_       =     tmpdirname_orig







			def     snake_case__(					self			:    Optional[int]							)				->Optional[Any]:
									snake_case_       =     self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name							)

									snake_case_       =     ('''a''' for _ in range(1_0_0_0							))
									snake_case_       =     tokenizer.train_new_from_iterator(text_iterator=_UpperCamelCase							,       length=1_0_0_0							,       vocab_size=5_0							)

									snake_case_       =     new_tokenizer.encode('''a🤗'''							)
									self.assertEqual(_UpperCamelCase							,       [6_4, 1_7_2, 2_5_3, 9_7, 2_4_5]							)






@require_tokenizers
class      snake_case_       (		unittest.TestCase ):







			'''simple docstring'''

			def     snake_case__(					self			:    Optional[int]							)				->int:
									snake_case_       =     AutoTokenizer.from_pretrained('''bert-base-cased'''							)
									snake_case_       =     json.loads(tokenizer._tokenizer.to_str()							)
									snake_case_       =     len(_UpperCamelCase							)

									with tempfile.TemporaryDirectory() as tmp_dir:
															# Hack to save this in the tokenizer_config.json
															snake_case_       =     ['''tokenizer.4.0.0.json''']
															tokenizer.save_pretrained(_UpperCamelCase							)
															json.dump(_UpperCamelCase							,       open(os.path.join(_UpperCamelCase							,       '''tokenizer.4.0.0.json'''							)							,       '''w'''							)							)

															# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
															snake_case_       =     AutoTokenizer.from_pretrained(_UpperCamelCase							)
															self.assertEqual(len(_UpperCamelCase							)							,       len(_UpperCamelCase							) + 1							)
															snake_case_       =     json.loads(new_tokenizer._tokenizer.to_str()							)
															self.assertIn('''huggingface'''							,       json_tokenizer['''model''']['''vocab''']							)

															# Will need to be adjusted if we reach v42 and this test is still here.
															# Should pick the old tokenizer file as the version of Transformers is < 4.0.0
															shutil.move(os.path.join(_UpperCamelCase							,       '''tokenizer.4.0.0.json'''							)							,       os.path.join(_UpperCamelCase							,       '''tokenizer.42.0.0.json'''							)							)
															snake_case_       =     ['''tokenizer.42.0.0.json''']
															tokenizer.save_pretrained(_UpperCamelCase							)
															snake_case_       =     AutoTokenizer.from_pretrained(_UpperCamelCase							)
															self.assertEqual(len(_UpperCamelCase							)							,       len(_UpperCamelCase							)							)
															snake_case_       =     json.loads(new_tokenizer._tokenizer.to_str()							)
															self.assertNotIn('''huggingface'''							,       json_tokenizer['''model''']['''vocab''']							)







			def     snake_case__(					self			:    List[str]							)				->Optional[Any]:
									# This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
									snake_case_       =     '''hf-internal-testing/test-two-tokenizers'''

									# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
									snake_case_       =     AutoTokenizer.from_pretrained(_UpperCamelCase							)
									self.assertEqual(len(_UpperCamelCase							)							,       2_8_9_9_7							)
									snake_case_       =     json.loads(tokenizer._tokenizer.to_str()							)
									self.assertIn('''huggingface'''							,       json_tokenizer['''model''']['''vocab''']							)

									# Testing an older version by monkey-patching the version in the module it's used.
									import transformers as old_transformers

									snake_case_       =     '''3.0.0'''
									snake_case_       =     old_transformers.models.auto.AutoTokenizer.from_pretrained(_UpperCamelCase							)
									self.assertEqual(len(_UpperCamelCase							)							,       2_8_9_9_6							)
									snake_case_       =     json.loads(old_tokenizer._tokenizer.to_str()							)
									self.assertNotIn('''huggingface'''							,       json_tokenizer['''model''']['''vocab''']							)






@require_tokenizers
class      snake_case_       (		unittest.TestCase ):







			'''simple docstring'''

			def     snake_case__(					self			:    str							)				->Optional[int]:
									# See https://github.com/huggingface/transformers/pull/12550
									# and https://github.com/huggingface/tokenizers/issues/537
									snake_case_       =     PreTrainedTokenizerFast.from_pretrained('''robot-test/dummy-tokenizer-wordlevel'''							)
									snake_case_       =     '''The Matrix is a 1999 science fiction action film.'''

									with concurrent.futures.ThreadPoolExecutor() as executor:
															snake_case_       =     [executor.submit(self.fetch							,       _UpperCamelCase							,       _UpperCamelCase							) for i in range(1_0							)]
															snake_case_       =     [future.result() for future in futures]
															self.assertEqual(_UpperCamelCase							,       [[1, 1_0, 0, 8, 0, 1_8, 0, 0, 0, 2] for i in range(1_0							)]							)







			def     snake_case__(					self			:    Optional[int]							,       _UpperCamelCase			:    Dict							,       _UpperCamelCase			:    str							)				->Tuple:
									return tokenizer.encode(_UpperCamelCase							,       truncation='''longest_first'''							,       padding='''longest'''							)


from __future__ import annotations

from random import randrange


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
						if len(SCREAMING_SNAKE_CASE__	) < 2:
												return collection
						snake_case_       =     randrange(len(SCREAMING_SNAKE_CASE__	)	)  # Use random element as pivot
						snake_case_       =     collection[pivot_index]
						snake_case_       =     []  # All elements greater than pivot
						snake_case_       =     []  # All elements less than or equal to pivot

						for element in collection[:pivot_index]:
												(greater if element > pivot else lesser).append(SCREAMING_SNAKE_CASE__	)

						for element in collection[pivot_index + 1 :]:
												(greater if element > pivot else lesser).append(SCREAMING_SNAKE_CASE__	)

						return [*quick_sort(SCREAMING_SNAKE_CASE__	), pivot, *quick_sort(SCREAMING_SNAKE_CASE__	)]


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