
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 __snake_case		(				unittest.TestCase					):
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				tempfile.mkdtemp()

           lowercase							:					Optional[Any]	     =				BlipImageProcessor()
           lowercase							:					Optional[int]	     =				GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""				)
           lowercase							:					Tuple	     =				BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert"""				)

           lowercase							:					Union[str, Any]	     =				InstructBlipProcessor(snake_case		,snake_case		,snake_case				)

           processor.save_pretrained(self.tmpdirname				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,**snake_case				):



           '''simple docstring'''







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



           '''simple docstring'''







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



           '''simple docstring'''







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



           '''simple docstring'''







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



           '''simple docstring'''







           lowercase							:					List[Any]	     =				[np.random.randint(255		,size=(3, 30, 400)		,dtype=np.uinta				)]

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

           return image_inputs
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







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

           lowercase							:					Optional[Any]	     =				self.get_tokenizer(bos_token="""(BOS)"""		,eos_token="""(EOS)"""				)
           lowercase							:					Dict	     =				self.get_image_processor(do_normalize=snake_case		,padding_value=1.0				)

           lowercase							:					Union[str, Any]	     =				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 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Any	     =				self.get_image_processor()
           lowercase							:					List[str]	     =				self.get_tokenizer()
           lowercase							:					Union[str, Any]	     =				self.get_qformer_tokenizer()

           lowercase							:					Dict	     =				InstructBlipProcessor(
               tokenizer=snake_case		,image_processor=snake_case		,qformer_tokenizer=snake_case				)

           lowercase							:					List[str]	     =				self.prepare_image_inputs()

           lowercase							:					Any	     =				image_processor(snake_case		,return_tensors="""np"""				)
           lowercase							:					Any	     =				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 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				self.get_image_processor()
           lowercase							:					int	     =				self.get_tokenizer()
           lowercase							:					List[str]	     =				self.get_qformer_tokenizer()

           lowercase							:					Dict	     =				InstructBlipProcessor(
               tokenizer=snake_case		,image_processor=snake_case		,qformer_tokenizer=snake_case				)

           lowercase							:					str	     =				"""lower newer"""

           lowercase							:					int	     =				processor(text=snake_case				)

           lowercase							:					Any	     =				tokenizer(snake_case		,return_token_type_ids=snake_case				)
           lowercase							:					Tuple	     =				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 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				self.get_image_processor()
           lowercase							:					int	     =				self.get_tokenizer()
           lowercase							:					List[str]	     =				self.get_qformer_tokenizer()

           lowercase							:					Optional[int]	     =				InstructBlipProcessor(
               tokenizer=snake_case		,image_processor=snake_case		,qformer_tokenizer=snake_case				)

           lowercase							:					Tuple	     =				"""lower newer"""
           lowercase							:					Any	     =				self.prepare_image_inputs()

           lowercase							:					List[Any]	     =				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 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				self.get_image_processor()
           lowercase							:					Union[str, Any]	     =				self.get_tokenizer()
           lowercase							:					Optional[int]	     =				self.get_qformer_tokenizer()

           lowercase							:					Any	     =				InstructBlipProcessor(
               tokenizer=snake_case		,image_processor=snake_case		,qformer_tokenizer=snake_case				)

           lowercase							:					Optional[Any]	     =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           lowercase							:					List[str]	     =				processor.batch_decode(snake_case				)
           lowercase							:					int	     =				tokenizer.batch_decode(snake_case				)

           self.assertListEqual(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				self.get_image_processor()
           lowercase							:					List[str]	     =				self.get_tokenizer()
           lowercase							:					Tuple	     =				self.get_qformer_tokenizer()

           lowercase							:					List[str]	     =				InstructBlipProcessor(
               tokenizer=snake_case		,image_processor=snake_case		,qformer_tokenizer=snake_case				)

           lowercase							:					Optional[Any]	     =				"""lower newer"""
           lowercase							:					Any	     =				self.prepare_image_inputs()

           lowercase							:					Optional[Any]	     =				processor(text=snake_case		,images=snake_case				)

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


from collections.abc import Generator







def 						_snake_case(  )			->   Generator[int, None, None]:
       lowercase     ,      lowercase							:					List[str]	     =				0, 1
       while True:
              lowercase     ,      lowercase							:					Optional[int]	     =				b, a + b
              yield b







def 						_snake_case(  SCREAMING_SNAKE_CASE__ = 1_000    )			->   int:
       lowercase							:					List[Any]	     =				1
       lowercase							:					Tuple	     =				fibonacci_generator()
       while len(str(next(SCREAMING_SNAKE_CASE__    )    )    ) < n:
              answer += 1
       return answer + 1


if __name__ == "__main__":
  print(solution(int(str(input()).strip())))
