



"""simple docstring"""







from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
A    =			1_0

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							str:
							for i in range(lowercase__				,      lowercase__			):
														if array[i] == target:
																					return i
							return -1

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							List[str]:
							A         =					0
							A         =					len(lowercase__			)
							while left <= right:
														if right - left < precision:
																					return lin_search(lowercase__				,      lowercase__				,      lowercase__				,      lowercase__			)

														A         =					(left + right) // 3 + 1
														A         =					2 * (left + right) // 3 + 1

														if array[one_third] == target:
																					return one_third
														elif array[two_third] == target:
																					return two_third

														elif target < array[one_third]:
																					A         =					one_third - 1
														elif array[two_third] < target:
																					A         =					two_third + 1

														else:
																					A         =					one_third + 1
																					A         =					two_third - 1
							else:
														return -1

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							int:
							if left < right:
														if right - left < precision:
																					return lin_search(lowercase__				,      lowercase__				,      lowercase__				,      lowercase__			)
														A         =					(left + right) // 3 + 1
														A         =					2 * (left + right) // 3 + 1

														if array[one_third] == target:
																					return one_third
														elif array[two_third] == target:
																					return two_third

														elif target < array[one_third]:
																					return rec_ternary_search(lowercase__				,      one_third - 1				,      lowercase__				,      lowercase__			)
														elif array[two_third] < target:
																					return rec_ternary_search(two_third + 1				,      lowercase__				,      lowercase__				,      lowercase__			)
														else:
																					return rec_ternary_search(one_third + 1				,      two_third - 1				,      lowercase__				,      lowercase__			)
							else:
														return -1


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							A    =			input('Enter numbers separated by comma:\n').strip()
							A    =			[int(item.strip()) for item in user_input.split(',')]
							assert collection == sorted(collection), F"List must be ordered.\n{collection}."
							A    =			int(input('Enter the number to be found in the list:\n').strip())
							A    =			ite_ternary_search(collection, target)
							A    =			rec_ternary_search(0, len(collection) - 1, collection, target)
							if resulta != -1:
														print(F'Iterative search: {target} found at positions: {resulta}')
														print(F'Recursive search: {target} found at positions: {resulta}')
							else:
														print('Not found')











"""simple docstring"""







import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import (
							    AutoProcessor,
							    BertTokenizerFast,
							    BlipImageProcessor,
							    GPTaTokenizer,
							    InstructBlipProcessor,
							    PreTrainedTokenizerFast,
							)






@require_vision
class      UpperCAmelCase__		(      unittest.TestCase     ):



		def 					A_		( self  :    Dict				)			->						Dict:






									'''simple docstring'''

									A         =					tempfile.mkdtemp()

									A         =					BlipImageProcessor()
									A         =					GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'				)
									A         =					BertTokenizerFast.from_pretrained('hf-internal-testing/tiny-random-bert'				)

									A         =					InstructBlipProcessor(snake_case      ,							snake_case      ,							snake_case				)

									processor.save_pretrained(self.tmpdirname				)



		def 					A_		( self  :    List[str]      ,							**snake_case  :    str				)			->						Dict:






									'''simple docstring'''

									return AutoProcessor.from_pretrained(self.tmpdirname      ,							**snake_case				).tokenizer



		def 					A_		( self  :    int      ,							**snake_case  :    Optional[Any]				)			->						Any:






									'''simple docstring'''

									return AutoProcessor.from_pretrained(self.tmpdirname      ,							**snake_case				).image_processor



		def 					A_		( self  :    Any      ,							**snake_case  :    Union[str, Any]				)			->						Any:






									'''simple docstring'''

									return AutoProcessor.from_pretrained(self.tmpdirname      ,							**snake_case				).qformer_tokenizer



		def 					A_		( self  :    int				)			->						Union[str, Any]:






									'''simple docstring'''

									shutil.rmtree(self.tmpdirname				)



		def 					A_		( self  :    List[Any]				)			->						Tuple:






									'''simple docstring'''

									A         =					[np.random.randint(255      ,							size=(3, 30, 400)      ,							dtype=np.uinta				)]

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

									return image_inputs



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






									'''simple docstring'''

									A         =					InstructBlipProcessor(
									    tokenizer=self.get_tokenizer()      ,							image_processor=self.get_image_processor()      ,							qformer_tokenizer=self.get_qformer_tokenizer()      ,							)
									processor.save_pretrained(self.tmpdirname				)

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

									A         =					InstructBlipProcessor.from_pretrained(
									    self.tmpdirname      ,							bos_token='(BOS)'      ,							eos_token='(EOS)'      ,							do_normalize=snake_case      ,							padding_value=1.0				)

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

									self.assertEqual(processor.image_processor.to_json_string()      ,							image_processor_add_kwargs.to_json_string()				)
									self.assertIsInstance(processor.image_processor      ,							snake_case				)
									self.assertIsInstance(processor.qformer_tokenizer      ,							snake_case				)



		def 					A_		( self  :    Optional[Any]				)			->						Dict:






									'''simple docstring'''

									A         =					self.get_image_processor()
									A         =					self.get_tokenizer()
									A         =					self.get_qformer_tokenizer()

									A         =					InstructBlipProcessor(
									    tokenizer=snake_case      ,							image_processor=snake_case      ,							qformer_tokenizer=snake_case				)

									A         =					self.prepare_image_inputs()

									A         =					image_processor(snake_case      ,							return_tensors='np'				)
									A         =					processor(images=snake_case      ,							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  :    Tuple				)			->						List[str]:






									'''simple docstring'''

									A         =					self.get_image_processor()
									A         =					self.get_tokenizer()
									A         =					self.get_qformer_tokenizer()

									A         =					InstructBlipProcessor(
									    tokenizer=snake_case      ,							image_processor=snake_case      ,							qformer_tokenizer=snake_case				)

									A         =					'lower newer'

									A         =					processor(text=snake_case				)

									A         =					tokenizer(snake_case      ,							return_token_type_ids=snake_case				)
									A         =					qformer_tokenizer(snake_case      ,							return_token_type_ids=snake_case				)

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

									for key in encoded_tokens_qformer.keys():
																self.assertListEqual(encoded_tokens_qformer[key]      ,							encoded_processor['qformer_' + key]				)



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






									'''simple docstring'''

									A         =					self.get_image_processor()
									A         =					self.get_tokenizer()
									A         =					self.get_qformer_tokenizer()

									A         =					InstructBlipProcessor(
									    tokenizer=snake_case      ,							image_processor=snake_case      ,							qformer_tokenizer=snake_case				)

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

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

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

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



		def 					A_		( self  :    Optional[Any]				)			->						List[str]:






									'''simple docstring'''

									A         =					self.get_image_processor()
									A         =					self.get_tokenizer()
									A         =					self.get_qformer_tokenizer()

									A         =					InstructBlipProcessor(
									    tokenizer=snake_case      ,							image_processor=snake_case      ,							qformer_tokenizer=snake_case				)

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

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

									self.assertListEqual(snake_case      ,							snake_case				)



		def 					A_		( self  :    Tuple				)			->						List[Any]:






									'''simple docstring'''

									A         =					self.get_image_processor()
									A         =					self.get_tokenizer()
									A         =					self.get_qformer_tokenizer()

									A         =					InstructBlipProcessor(
									    tokenizer=snake_case      ,							image_processor=snake_case      ,							qformer_tokenizer=snake_case				)

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

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

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



