


'''simple docstring'''






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
							import torch


if is_vision_available():
							from PIL import Image

							from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class 						__UpperCamelCase	(						unittest.TestCase							):
							lowercase			:		List[Any]   		=  ViTImageProcessor if is_vision_available() else None





							@property
							def 		a__   (  self		:Optional[int]			):
									return self.image_processor_tester.prepare_image_processor_dict()





							def 		a__   (  self		:int			):
									snake_case_		:							Tuple			       =						(3, 3_2, 1_2_8)
									snake_case_		:							str			       =						tempfile.mkdtemp()

									# fmt: off
									snake_case_		:							Tuple			       =						['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
									# fmt: on
									snake_case_		:							Union[str, Any]			       =						dict(zip(_UpperCamelCase  ,range(len(_UpperCamelCase			)			)			)			)

									snake_case_		:							Tuple			       =						os.path.join(self.tmpdirname  ,VOCAB_FILES_NAMES["""vocab_file"""]			)
									with open(self.vocab_file  ,"""w"""  ,encoding="""utf-8"""			) as fp:
											fp.write(json.dumps(_UpperCamelCase			) + """\n"""			)

									snake_case_		:							Any			       =						{
									    '''do_normalize''': False,
									    '''do_resize''': True,
									    '''image_processor_type''': '''ViTImageProcessor''',
									    '''resample''': 3,
									    '''size''': {'''height''': 3_2, '''width''': 1_2_8},
									}
									snake_case_		:							List[Any]			       =						os.path.join(self.tmpdirname  ,_UpperCamelCase			)
									with open(self.image_processor_file  ,"""w"""  ,encoding="""utf-8"""			) as fp:
											json.dump(_UpperCamelCase  ,_UpperCamelCase			)





							def 		a__   (  self		:Dict  ,**_UpperCamelCase		:Tuple			):
									return MgpstrTokenizer.from_pretrained(self.tmpdirname  ,**_UpperCamelCase			)





							def 		a__   (  self		:str  ,**_UpperCamelCase		:Optional[Any]			):
									return ViTImageProcessor.from_pretrained(self.tmpdirname  ,**_UpperCamelCase			)





							def 		a__   (  self		:List[str]			):
									shutil.rmtree(self.tmpdirname			)





							def 		a__   (  self		:Optional[int]			):
									snake_case_		:							Any			       =						np.random.randint(2_5_5  ,size=(3, 3_0, 4_0_0)  ,dtype=np.uinta			)

									snake_case_		:							List[Any]			       =						Image.fromarray(np.moveaxis(_UpperCamelCase  ,0  ,-1			)			)

									return image_input





							def 		a__   (  self		:Any			):
									snake_case_		:							Tuple			       =						self.get_tokenizer()
									snake_case_		:							Dict			       =						self.get_image_processor()

									snake_case_		:							Tuple			       =						MgpstrProcessor(tokenizer=_UpperCamelCase  ,image_processor=_UpperCamelCase			)
									processor.save_pretrained(self.tmpdirname			)
									snake_case_		:							str			       =						MgpstrProcessor.from_pretrained(self.tmpdirname  ,use_fast=_UpperCamelCase			)

									self.assertEqual(processor.char_tokenizer.get_vocab()  ,tokenizer.get_vocab()			)
									self.assertIsInstance(processor.char_tokenizer  ,_UpperCamelCase			)

									self.assertEqual(processor.image_processor.to_json_string()  ,image_processor.to_json_string()			)
									self.assertIsInstance(processor.image_processor  ,_UpperCamelCase			)





							def 		a__   (  self		:str			):
									snake_case_		:							str			       =						self.get_tokenizer()
									snake_case_		:							List[Any]			       =						self.get_image_processor()

									snake_case_		:							Dict			       =						MgpstrProcessor(tokenizer=_UpperCamelCase  ,image_processor=_UpperCamelCase			)
									processor.save_pretrained(self.tmpdirname			)

									snake_case_		:							str			       =						self.get_tokenizer(bos_token="""(BOS)"""  ,eos_token="""(EOS)"""			)
									snake_case_		:							str			       =						self.get_image_processor(do_normalize=_UpperCamelCase  ,padding_value=1.0			)

									snake_case_		:							Any			       =						MgpstrProcessor.from_pretrained(
									    self.tmpdirname  ,bos_token="""(BOS)"""  ,eos_token="""(EOS)"""  ,do_normalize=_UpperCamelCase  ,padding_value=1.0			)

									self.assertEqual(processor.char_tokenizer.get_vocab()  ,tokenizer_add_kwargs.get_vocab()			)
									self.assertIsInstance(processor.char_tokenizer  ,_UpperCamelCase			)

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





							def 		a__   (  self		:List[Any]			):
									snake_case_		:							Optional[int]			       =						self.get_image_processor()
									snake_case_		:							Dict			       =						self.get_tokenizer()

									snake_case_		:							List[Any]			       =						MgpstrProcessor(tokenizer=_UpperCamelCase  ,image_processor=_UpperCamelCase			)

									snake_case_		:							Any			       =						self.prepare_image_inputs()

									snake_case_		:							List[Any]			       =						image_processor(_UpperCamelCase  ,return_tensors="""np"""			)
									snake_case_		:							str			       =						processor(images=_UpperCamelCase  ,return_tensors="""np"""			)

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





							def 		a__   (  self		:Union[str, Any]			):
									snake_case_		:							List[Any]			       =						self.get_image_processor()
									snake_case_		:							List[Any]			       =						self.get_tokenizer()

									snake_case_		:							str			       =						MgpstrProcessor(tokenizer=_UpperCamelCase  ,image_processor=_UpperCamelCase			)

									snake_case_		:							List[str]			       =						'''test'''

									snake_case_		:							Optional[int]			       =						processor(text=_UpperCamelCase			)

									snake_case_		:							Union[str, Any]			       =						tokenizer(_UpperCamelCase			)
									for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]  ,encoded_processor[key]			)





							def 		a__   (  self		:Optional[int]			):
									snake_case_		:							Optional[Any]			       =						self.get_image_processor()
									snake_case_		:							Optional[int]			       =						self.get_tokenizer()

									snake_case_		:							Optional[Any]			       =						MgpstrProcessor(tokenizer=_UpperCamelCase  ,image_processor=_UpperCamelCase			)

									snake_case_		:							Any			       =						'''test'''
									snake_case_		:							Any			       =						self.prepare_image_inputs()

									snake_case_		:							str			       =						processor(text=_UpperCamelCase  ,images=_UpperCamelCase			)

									self.assertListEqual(list(inputs.keys()			)  ,["""pixel_values""", """labels"""]			)

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





							def 		a__   (  self		:List[str]			):
									snake_case_		:							str			       =						self.get_image_processor()
									snake_case_		:							Optional[Any]			       =						self.get_tokenizer()

									snake_case_		:							Dict			       =						MgpstrProcessor(tokenizer=_UpperCamelCase  ,image_processor=_UpperCamelCase			)

									snake_case_		:							Optional[int]			       =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

									snake_case_		:							Optional[int]			       =						processor.char_decode(_UpperCamelCase			)
									snake_case_		:							Union[str, Any]			       =						tokenizer.batch_decode(_UpperCamelCase			)
									snake_case_		:							Tuple			       =						[seq.replace(""" """  ,""""""			) for seq in decoded_tok]

									self.assertListEqual(_UpperCamelCase  ,_UpperCamelCase			)





							def 		a__   (  self		:Tuple			):
									snake_case_		:							str			       =						self.get_image_processor()
									snake_case_		:							List[Any]			       =						self.get_tokenizer()

									snake_case_		:							Any			       =						MgpstrProcessor(tokenizer=_UpperCamelCase  ,image_processor=_UpperCamelCase			)

									snake_case_		:							str			       =						None
									snake_case_		:							Optional[int]			       =						self.prepare_image_inputs()

									snake_case_		:							Optional[int]			       =						processor(text=_UpperCamelCase  ,images=_UpperCamelCase			)

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





							def 		a__   (  self		:str			):
									snake_case_		:							Optional[Any]			       =						self.get_image_processor()
									snake_case_		:							Dict			       =						self.get_tokenizer()

									snake_case_		:							int			       =						MgpstrProcessor(tokenizer=_UpperCamelCase  ,image_processor=_UpperCamelCase			)

									snake_case_		:							str			       =						torch.randn(1  ,2_7  ,3_8			)
									snake_case_		:							List[Any]			       =						torch.randn(1  ,2_7  ,5_0_2_5_7			)
									snake_case_		:							Union[str, Any]			       =						torch.randn(1  ,2_7  ,3_0_5_2_2			)

									snake_case_		:							Union[str, Any]			       =						processor.batch_decode([char_input, bpe_input, wp_input]			)

									self.assertListEqual(list(results.keys()			)  ,["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""]			)



'''simple docstring'''






import math





def      UpperCAmelCase      (		lowerCamelCase_  :list				,      lowerCamelCase_  :int ):







		'''simple docstring'''
		snake_case_		:							Union[str, Any]			       =						len(lowerCamelCase_ )
		snake_case_		:							List[Any]			       =						int(math.floor(math.sqrt(lowerCamelCase_ ) ) )
		snake_case_		:							str			       =						0
		while arr[min(lowerCamelCase_				,      lowerCamelCase_ ) - 1] < x:
				snake_case_		:							Any			       =						step
				step += int(math.floor(math.sqrt(lowerCamelCase_ ) ) )
				if prev >= n:
						return -1

		while arr[prev] < x:
				snake_case_		:							Optional[Any]			       =						prev + 1
				if prev == min(lowerCamelCase_				,      lowerCamelCase_ ):
						return -1
		if arr[prev] == x:
				return prev
		return -1


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