






import json
import os
import shutil
import tempfile
import unittest

import numpy as np

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


if is_vision_available():
					from PIL import Image

					from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class      UpperCamelCase (				unittest.TestCase ):







		"""simple docstring"""



		def       A(  self		:       Optional[Any]		)    ->   Optional[int]:


						'''simple docstring'''
						A	 =      tempfile.mkdtemp()

						# fmt: off
						A	 =      ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
						# fmt: on
						A	 =      os.path.join(self.tmpdirname						,VOCAB_FILES_NAMES['vocab_file']		)
						with open(self.vocab_file						,'w'						,encoding='utf-8'		) as vocab_writer:
										vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]		)		)

						A	 =      {
						    'do_resize': True,
						    'size': {'height': 1_8, 'width': 1_8},
						    'do_normalize': True,
						    'image_mean': [0.5, 0.5, 0.5],
						    'image_std': [0.5, 0.5, 0.5],
						}
						A	 =      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		:       List[Any]						,**_SCREAMING_SNAKE_CASE		:       Any		)    ->   List[str]:


						'''simple docstring'''
						return BertTokenizer.from_pretrained(self.tmpdirname						,**__A		)
		def       A(  self		:       int						,**_SCREAMING_SNAKE_CASE		:       List[str]		)    ->   Optional[Any]:


						'''simple docstring'''
						return ViTImageProcessor.from_pretrained(self.tmpdirname						,**__A		)
		def       A(  self		:       Union[str, Any]		)    ->   Tuple:


						'''simple docstring'''
						shutil.rmtree(self.tmpdirname		)
		def       A(  self		:       Optional[int]		)    ->   Dict:


						'''simple docstring'''
						A	 =      [np.random.randint(2_5_5						,size=(3, 3_0, 4_0_0)						,dtype=np.uinta		)]

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

						return image_inputs
		def       A(  self		:       Any		)    ->   Dict:


						'''simple docstring'''
						A	 =      self.get_tokenizer()
						A	 =      self.get_image_processor()

						A	 =      VisionTextDualEncoderProcessor(tokenizer=__A						,image_processor=__A		)

						processor.save_pretrained(self.tmpdirname		)
						A	 =      VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname		)

						self.assertEqual(processor.tokenizer.get_vocab()						,tokenizer.get_vocab()		)
						self.assertIsInstance(processor.tokenizer						,(BertTokenizer, BertTokenizerFast)		)

						self.assertEqual(processor.image_processor.to_json_string()						,image_processor.to_json_string()		)
						self.assertIsInstance(processor.image_processor						,__A		)
		def       A(  self		:       Union[str, Any]		)    ->   int:


						'''simple docstring'''
						A	 =      VisionTextDualEncoderProcessor(
						    tokenizer=self.get_tokenizer()						,image_processor=self.get_image_processor()		)
						processor.save_pretrained(self.tmpdirname		)

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

						A	 =      VisionTextDualEncoderProcessor.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						,(BertTokenizer, BertTokenizerFast)		)

						self.assertEqual(processor.image_processor.to_json_string()						,image_processor_add_kwargs.to_json_string()		)
						self.assertIsInstance(processor.image_processor						,__A		)
		def       A(  self		:       Optional[Any]		)    ->   Optional[int]:


						'''simple docstring'''
						A	 =      self.get_image_processor()
						A	 =      self.get_tokenizer()

						A	 =      VisionTextDualEncoderProcessor(tokenizer=__A						,image_processor=__A		)

						A	 =      self.prepare_image_inputs()

						A	 =      image_processor(__A						,return_tensors='np'		)
						A	 =      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		)
		def       A(  self		:       int		)    ->   List[str]:


						'''simple docstring'''
						A	 =      self.get_image_processor()
						A	 =      self.get_tokenizer()

						A	 =      VisionTextDualEncoderProcessor(tokenizer=__A						,image_processor=__A		)

						A	 =      'lower newer'

						A	 =      processor(text=__A		)

						A	 =      tokenizer(__A		)

						for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]						,encoded_processor[key]		)
		def       A(  self		:       Optional[int]		)    ->   Any:


						'''simple docstring'''
						A	 =      self.get_image_processor()
						A	 =      self.get_tokenizer()

						A	 =      VisionTextDualEncoderProcessor(tokenizer=__A						,image_processor=__A		)

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

						A	 =      processor(text=__A						,images=__A		)

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

						# test if it raises when no input is passed
						with self.assertRaises(__A		):
										processor()
		def       A(  self		:       Union[str, Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						A	 =      self.get_image_processor()
						A	 =      self.get_tokenizer()

						A	 =      VisionTextDualEncoderProcessor(tokenizer=__A						,image_processor=__A		)

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

						A	 =      processor.batch_decode(__A		)
						A	 =      tokenizer.batch_decode(__A		)

						self.assertListEqual(__A						,__A		)







		def       A(  self		:       Optional[int]		)    ->   Dict:


						'''simple docstring'''
						A	 =      self.get_image_processor()
						A	 =      self.get_tokenizer()

						A	 =      VisionTextDualEncoderProcessor(tokenizer=__A						,image_processor=__A		)

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

						A	 =      processor(text=__A						,images=__A		)

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













import math







def      snake_case		(							UpperCAmelCase	:       list,						UpperCAmelCase	:       int   ):
				A	 =      len(UpperCAmelCase   )
				A	 =      int(math.floor(math.sqrt(UpperCAmelCase   )   )   )
				A	 =      0
				while arr[min(UpperCAmelCase,						UpperCAmelCase   ) - 1] < x:
								A	 =      step
								step += int(math.floor(math.sqrt(UpperCAmelCase   )   )   )
								if prev >= n:
												return -1

				while arr[prev] < x:
								A	 =      prev + 1
								if prev == min(UpperCAmelCase,						UpperCAmelCase   ):
												return -1
				if arr[prev] == x:
								return prev
				return -1


if __name__ == "__main__":
					lowerCAmelCase_           = input('Enter numbers separated by a comma:\n').strip()
					lowerCAmelCase_           = [int(item) for item in user_input.split(',')]
					lowerCAmelCase_           = int(input('Enter the number to be searched:\n'))
					lowerCAmelCase_           = jump_search(arr, x)
					if res == -1:
										print('Number not found!')
					else:
										print(f'''Number {x} is at index {res}''')





