




'''simple docstring'''




def 	__lowerCamelCase (  UpperCAmelCase_       )      ->str:
	snake_case__  					=      len(UpperCAmelCase_       )
	for i in range(length - 1       ):
		snake_case__  					=      False
		for j in range(length - 1 - i       ):
			if collection[j] > collection[j + 1]:
				snake_case__  					=      True
				snake_case__					,     snake_case__  					=      collection[j + 1], collection[j]
		if not swapped:
			break  # Stop iteration if the collection is sorted.
	return collection


if __name__ == "__main__":
			import doctest
			import time

			doctest.testmod()

			a__				: Dict									=    input('''Enter numbers separated by a comma:''').strip()
			a__				: Dict									=    [int(item) for item in user_input.split(''',''')]
			a__				: Tuple									=    time.process_time()
			print(*bubble_sort(unsorted), sep=''',''')
			print(f"""Processing time: {(time.process_time() - start)%1e9 + 7}""")










'''simple docstring'''




import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import FlavaImageProcessor, FlavaProcessor
			from transformers.models.flava.image_processing_flava import (
			    FLAVA_CODEBOOK_MEAN,
			    FLAVA_CODEBOOK_STD,
			    FLAVA_IMAGE_MEAN,
			    FLAVA_IMAGE_STD,
			)






@require_vision
class 							__snake_case    (				unittest.TestCase       ):





	def      _snake_case      (		self		)			->		Optional[Any]:
		snake_case__  					=      tempfile.mkdtemp()

		# fmt: off
		snake_case__  					=      ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
		# fmt: on
		snake_case__  					=      os.path.join(self.tmpdirname			,					VOCAB_FILES_NAMES['vocab_file']		)

		with open(self.vocab_file			,					'w'			,					encoding='utf-8'		) as fp:
			fp.write(''.join([x + '\n' for x in vocab_tokens]		)		)

		snake_case__  					=      {
		    'image_mean': FLAVA_IMAGE_MEAN,
		    'image_std': FLAVA_IMAGE_STD,
		    'do_normalize': True,
		    'do_resize': True,
		    'size': 224,
		    'do_center_crop': True,
		    'crop_size': 224,
		    'input_size_patches': 14,
		    'total_mask_patches': 75,
		    'mask_group_max_patches': None,
		    'mask_group_min_patches': 16,
		    'mask_group_min_aspect_ratio': 0.3,
		    'mask_group_max_aspect_ratio': None,
		    'codebook_do_resize': True,
		    'codebook_size': 112,
		    'codebook_do_center_crop': True,
		    'codebook_crop_size': 112,
		    'codebook_do_map_pixels': True,
		    'codebook_do_normalize': True,
		    'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
		    'codebook_image_std': FLAVA_CODEBOOK_STD,
		}

		snake_case__  					=      os.path.join(self.tmpdirname			,					UpperCamelCase_		)
		with open(self.image_processor_file			,					'w'			,					encoding='utf-8'		) as fp:
			json.dump(UpperCamelCase_			,					UpperCamelCase_		)





	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		List[str]:
		return BertTokenizer.from_pretrained(self.tmpdirname			,					**UpperCamelCase_		)





	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		Optional[Any]:
		return BertTokenizerFast.from_pretrained(self.tmpdirname			,					**UpperCamelCase_		)





	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		Any:
		return FlavaImageProcessor.from_pretrained(self.tmpdirname			,					**UpperCamelCase_		)





	def      _snake_case      (		self		)			->		Union[str, Any]:
		shutil.rmtree(self.tmpdirname		)





	def      _snake_case      (		self		)			->		List[str]:
		snake_case__  					=      [np.random.randint(255			,					size=(3, 30, 400)			,					dtype=np.uinta		)]

		snake_case__  					=      [Image.fromarray(np.moveaxis(UpperCamelCase_			,					0			,					-1		)		) for x in image_inputs]

		return image_inputs





	def      _snake_case      (		self		)			->		Optional[int]:
		snake_case__  					=      self.get_tokenizer()
		snake_case__  					=      self.get_rust_tokenizer()
		snake_case__  					=      self.get_image_processor()

		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
		processor_slow.save_pretrained(self.tmpdirname		)
		snake_case__  					=      FlavaProcessor.from_pretrained(self.tmpdirname			,					use_fast=UpperCamelCase_		)

		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
		processor_fast.save_pretrained(self.tmpdirname		)
		snake_case__  					=      FlavaProcessor.from_pretrained(self.tmpdirname		)

		self.assertEqual(processor_slow.tokenizer.get_vocab()			,					tokenizer_slow.get_vocab()		)
		self.assertEqual(processor_fast.tokenizer.get_vocab()			,					tokenizer_fast.get_vocab()		)
		self.assertEqual(tokenizer_slow.get_vocab()			,					tokenizer_fast.get_vocab()		)
		self.assertIsInstance(processor_slow.tokenizer			,					UpperCamelCase_		)
		self.assertIsInstance(processor_fast.tokenizer			,					UpperCamelCase_		)

		self.assertEqual(processor_slow.image_processor.to_json_string()			,					image_processor.to_json_string()		)
		self.assertEqual(processor_fast.image_processor.to_json_string()			,					image_processor.to_json_string()		)
		self.assertIsInstance(processor_slow.image_processor			,					UpperCamelCase_		)
		self.assertIsInstance(processor_fast.image_processor			,					UpperCamelCase_		)





	def      _snake_case      (		self		)			->		List[str]:
		snake_case__  					=      FlavaProcessor(tokenizer=self.get_tokenizer()			,					image_processor=self.get_image_processor()		)
		processor.save_pretrained(self.tmpdirname		)

		snake_case__  					=      self.get_tokenizer(bos_token='(BOS)'			,					eos_token='(EOS)'		)
		snake_case__  					=      self.get_image_processor(do_normalize=UpperCamelCase_			,					padding_value=1.0		)

		snake_case__  					=      FlavaProcessor.from_pretrained(
		    self.tmpdirname			,					bos_token='(BOS)'			,					eos_token='(EOS)'			,					do_normalize=UpperCamelCase_			,					padding_value=1.0		)

		self.assertEqual(processor.tokenizer.get_vocab()			,					tokenizer_add_kwargs.get_vocab()		)
		self.assertIsInstance(processor.tokenizer			,					UpperCamelCase_		)

		self.assertEqual(processor.image_processor.to_json_string()			,					image_processor_add_kwargs.to_json_string()		)
		self.assertIsInstance(processor.image_processor			,					UpperCamelCase_		)





	def      _snake_case      (		self		)			->		Union[str, Any]:
		snake_case__  					=      self.get_image_processor()
		snake_case__  					=      self.get_tokenizer()

		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)

		snake_case__  					=      self.prepare_image_inputs()

		snake_case__  					=      image_processor(UpperCamelCase_			,					return_tensors='np'		)
		snake_case__  					=      processor(images=UpperCamelCase_			,					return_tensors='np'		)

		for key in input_feat_extract.keys():
			self.assertAlmostEqual(input_feat_extract[key].sum()			,					input_processor[key].sum()			,					delta=1E-2		)

		# With rest of the args
		random.seed(1234		)
		snake_case__  					=      image_processor(
		    UpperCamelCase_			,					return_image_mask=UpperCamelCase_			,					return_codebook_pixels=UpperCamelCase_			,					return_tensors='np'		)
		random.seed(1234		)
		snake_case__  					=      processor(
		    images=UpperCamelCase_			,					return_image_mask=UpperCamelCase_			,					return_codebook_pixels=UpperCamelCase_			,					return_tensors='np'		)

		for key in input_feat_extract.keys():
			self.assertAlmostEqual(input_feat_extract[key].sum()			,					input_processor[key].sum()			,					delta=1E-2		)





	def      _snake_case      (		self		)			->		Dict:
		snake_case__  					=      self.get_image_processor()
		snake_case__  					=      self.get_tokenizer()

		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)

		snake_case__  					=      'lower newer'

		snake_case__  					=      processor(text=UpperCamelCase_		)

		snake_case__  					=      tokenizer(UpperCamelCase_		)

		for key in encoded_tok.keys():
			self.assertListEqual(encoded_tok[key]			,					encoded_processor[key]		)





	def      _snake_case      (		self		)			->		Union[str, Any]:
		snake_case__  					=      self.get_image_processor()
		snake_case__  					=      self.get_tokenizer()

		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)

		snake_case__  					=      'lower newer'
		snake_case__  					=      self.prepare_image_inputs()

		snake_case__  					=      processor(text=UpperCamelCase_			,					images=UpperCamelCase_		)

		self.assertListEqual(list(inputs.keys()		)			,					['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']		)

		# add extra args
		snake_case__  					=      processor(text=UpperCamelCase_			,					images=UpperCamelCase_			,					return_codebook_pixels=UpperCamelCase_			,					return_image_mask=UpperCamelCase_		)

		self.assertListEqual(
		    list(inputs.keys()		)			,					[
		        'input_ids',
		        'token_type_ids',
		        'attention_mask',
		        'pixel_values',
		        'codebook_pixel_values',
		        'bool_masked_pos',
		    ]			,					)

		# test if it raises when no input is passed
		with pytest.raises(UpperCamelCase_		):
			processor()





	def      _snake_case      (		self		)			->		List[Any]:
		snake_case__  					=      self.get_image_processor()
		snake_case__  					=      self.get_tokenizer()

		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)

		snake_case__  					=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

		snake_case__  					=      processor.batch_decode(UpperCamelCase_		)
		snake_case__  					=      tokenizer.batch_decode(UpperCamelCase_		)

		self.assertListEqual(UpperCamelCase_			,					UpperCamelCase_		)





	def      _snake_case      (		self		)			->		Dict:
		snake_case__  					=      self.get_image_processor()
		snake_case__  					=      self.get_tokenizer()

		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)

		snake_case__  					=      'lower newer'
		snake_case__  					=      self.prepare_image_inputs()

		snake_case__  					=      processor(text=UpperCamelCase_			,					images=UpperCamelCase_		)

		self.assertListEqual(list(inputs.keys()		)			,					processor.model_input_names		)



