

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				):


							"""simple docstring"""





							def 							A__	(			self				):
												__lowerCAmelCase       =				tempfile.mkdtemp()

												# fmt: off
												__lowerCAmelCase       =				['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
												# fmt: on
												__lowerCAmelCase       =				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]				)				)

												__lowerCAmelCase       =				{
												    'image_mean': FLAVA_IMAGE_MEAN,
												    'image_std': FLAVA_IMAGE_STD,
												    'do_normalize': True,
												    'do_resize': True,
												    'size': 2_2_4,
												    'do_center_crop': True,
												    'crop_size': 2_2_4,
												    'input_size_patches': 1_4,
												    'total_mask_patches': 7_5,
												    'mask_group_max_patches': None,
												    'mask_group_min_patches': 1_6,
												    'mask_group_min_aspect_ratio': 0.3,
												    'mask_group_max_aspect_ratio': None,
												    'codebook_do_resize': True,
												    'codebook_size': 1_1_2,
												    'codebook_do_center_crop': True,
												    'codebook_crop_size': 1_1_2,
												    'codebook_do_map_pixels': True,
												    'codebook_do_normalize': True,
												    'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
												    'codebook_image_std': FLAVA_CODEBOOK_STD,
												}

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




							def 							A__	(			self       ,    **_A				):
												return BertTokenizer.from_pretrained(self.tmpdirname       ,    **_A				)




							def 							A__	(			self       ,    **_A				):
												return BertTokenizerFast.from_pretrained(self.tmpdirname       ,    **_A				)




							def 							A__	(			self       ,    **_A				):
												return FlavaImageProcessor.from_pretrained(self.tmpdirname       ,    **_A				)




							def 							A__	(			self				):
												shutil.rmtree(self.tmpdirname				)




							def 							A__	(			self				):
												__lowerCAmelCase       =				[np.random.randint(2_5_5       ,    size=(3, 3_0, 4_0_0)       ,    dtype=np.uinta				)]

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

												return image_inputs




							def 							A__	(			self				):
												__lowerCAmelCase       =				self.get_tokenizer()
												__lowerCAmelCase       =				self.get_rust_tokenizer()
												__lowerCAmelCase       =				self.get_image_processor()

												__lowerCAmelCase       =				FlavaProcessor(tokenizer=_A       ,    image_processor=_A				)
												processor_slow.save_pretrained(self.tmpdirname				)
												__lowerCAmelCase       =				FlavaProcessor.from_pretrained(self.tmpdirname       ,    use_fast=_A				)

												__lowerCAmelCase       =				FlavaProcessor(tokenizer=_A       ,    image_processor=_A				)
												processor_fast.save_pretrained(self.tmpdirname				)
												__lowerCAmelCase       =				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       ,    _A				)
												self.assertIsInstance(processor_fast.tokenizer       ,    _A				)

												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       ,    _A				)
												self.assertIsInstance(processor_fast.image_processor       ,    _A				)




							def 							A__	(			self				):
												__lowerCAmelCase       =				FlavaProcessor(tokenizer=self.get_tokenizer()       ,    image_processor=self.get_image_processor()				)
												processor.save_pretrained(self.tmpdirname				)

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

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

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

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




							def 							A__	(			self				):
												__lowerCAmelCase       =				self.get_image_processor()
												__lowerCAmelCase       =				self.get_tokenizer()

												__lowerCAmelCase       =				FlavaProcessor(tokenizer=_A       ,    image_processor=_A				)

												__lowerCAmelCase       =				self.prepare_image_inputs()

												__lowerCAmelCase       =				image_processor(_A       ,    return_tensors='np'				)
												__lowerCAmelCase       =				processor(images=_A       ,    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(1_2_3_4				)
												__lowerCAmelCase       =				image_processor(
												    _A       ,    return_image_mask=_A       ,    return_codebook_pixels=_A       ,    return_tensors='np'				)
												random.seed(1_2_3_4				)
												__lowerCAmelCase       =				processor(
												    images=_A       ,    return_image_mask=_A       ,    return_codebook_pixels=_A       ,    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 							A__	(			self				):
												__lowerCAmelCase       =				self.get_image_processor()
												__lowerCAmelCase       =				self.get_tokenizer()

												__lowerCAmelCase       =				FlavaProcessor(tokenizer=_A       ,    image_processor=_A				)

												__lowerCAmelCase       =				'lower newer'

												__lowerCAmelCase       =				processor(text=_A				)

												__lowerCAmelCase       =				tokenizer(_A				)

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




							def 							A__	(			self				):
												__lowerCAmelCase       =				self.get_image_processor()
												__lowerCAmelCase       =				self.get_tokenizer()

												__lowerCAmelCase       =				FlavaProcessor(tokenizer=_A       ,    image_processor=_A				)

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

												__lowerCAmelCase       =				processor(text=_A       ,    images=_A				)

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

												# add extra args
												__lowerCAmelCase       =				processor(text=_A       ,    images=_A       ,    return_codebook_pixels=_A       ,    return_image_mask=_A				)

												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(_A				):
																	processor()




							def 							A__	(			self				):
												__lowerCAmelCase       =				self.get_image_processor()
												__lowerCAmelCase       =				self.get_tokenizer()

												__lowerCAmelCase       =				FlavaProcessor(tokenizer=_A       ,    image_processor=_A				)

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

												__lowerCAmelCase       =				processor.batch_decode(_A				)
												__lowerCAmelCase       =				tokenizer.batch_decode(_A				)

												self.assertListEqual(_A       ,    _A				)




							def 							A__	(			self				):
												__lowerCAmelCase       =				self.get_image_processor()
												__lowerCAmelCase       =				self.get_tokenizer()

												__lowerCAmelCase       =				FlavaProcessor(tokenizer=_A       ,    image_processor=_A				)

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

												__lowerCAmelCase       =				processor(text=_A       ,    images=_A				)

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









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))






