

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
  from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
  _a	 =     False

if is_vision_available():
  from PIL import Image

  from transformers import (
      AutoProcessor,
      PixaStructImageProcessor,
      PixaStructProcessor,
      PreTrainedTokenizerFast,
      TaTokenizer,
  )






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11							,			reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'							,			)
@require_vision
@require_torch
class 							A_				(	unittest.TestCase      ):


     def 						UpperCAmelCase						(							self    :	Tuple      )							->			str:
      __lowerCAmelCase:     List[str]     			=   tempfile.mkdtemp()

      __lowerCAmelCase:     Dict     			=   PixaStructImageProcessor()
      __lowerCAmelCase:     Any     			=   TaTokenizer.from_pretrained('t5-small'      )

      __lowerCAmelCase:     List[str]     			=   PixaStructProcessor(UpperCAmelCase    ,						UpperCAmelCase      )

      processor.save_pretrained(self.tmpdirname      )


     def 						UpperCAmelCase						(							self    :	List[str]    ,						**UpperCAmelCase    :	Any      )							->			Optional[Any]:
      return AutoProcessor.from_pretrained(self.tmpdirname    ,						**UpperCAmelCase      ).tokenizer


     def 						UpperCAmelCase						(							self    :	Optional[Any]    ,						**UpperCAmelCase    :	Tuple      )							->			Any:
      return AutoProcessor.from_pretrained(self.tmpdirname    ,						**UpperCAmelCase      ).image_processor


     def 						UpperCAmelCase						(							self    :	Dict      )							->			str:
      shutil.rmtree(self.tmpdirname      )


     def 						UpperCAmelCase						(							self    :	Any      )							->			List[str]:
      __lowerCAmelCase:     str     			=   [np.random.randint(2_5_5    ,						size=(3, 3_0, 4_0_0)    ,						dtype=np.uinta      )]

      __lowerCAmelCase:     Union[str, Any]     			=   [Image.fromarray(np.moveaxis(UpperCAmelCase    ,						0    ,						-1      )      ) for x in image_inputs]

      return image_inputs


     def 						UpperCAmelCase						(							self    :	int      )							->			List[Any]:
      __lowerCAmelCase:     Dict     			=   PixaStructProcessor(tokenizer=self.get_tokenizer()    ,						image_processor=self.get_image_processor()      )
      processor.save_pretrained(self.tmpdirname      )

      __lowerCAmelCase:     str     			=   self.get_tokenizer(bos_token='(BOS)'    ,						eos_token='(EOS)'      )
      __lowerCAmelCase:     List[Any]     			=   self.get_image_processor(do_normalize=UpperCAmelCase    ,						padding_value=1.0      )

      __lowerCAmelCase:     Tuple     			=   PixaStructProcessor.from_pretrained(
          self.tmpdirname    ,						bos_token='(BOS)'    ,						eos_token='(EOS)'    ,						do_normalize=UpperCAmelCase    ,						padding_value=1.0      )

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

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


     def 						UpperCAmelCase						(							self    :	int      )							->			Any:
      __lowerCAmelCase:     List[Any]     			=   self.get_image_processor()
      __lowerCAmelCase:     int     			=   self.get_tokenizer()

      __lowerCAmelCase:     Tuple     			=   PixaStructProcessor(tokenizer=UpperCAmelCase    ,						image_processor=UpperCAmelCase      )

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

      __lowerCAmelCase:     Tuple     			=   image_processor(UpperCAmelCase    ,						return_tensors='np'      )
      __lowerCAmelCase:     Dict     			=   processor(images=UpperCAmelCase    ,						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 						UpperCAmelCase						(							self    :	List[Any]      )							->			str:
      __lowerCAmelCase:     Optional[int]     			=   self.get_image_processor()
      __lowerCAmelCase:     Union[str, Any]     			=   self.get_tokenizer()

      __lowerCAmelCase:     Union[str, Any]     			=   PixaStructProcessor(tokenizer=UpperCAmelCase    ,						image_processor=UpperCAmelCase      )

      __lowerCAmelCase:     List[str]     			=   'lower newer'

      __lowerCAmelCase:     Optional[Any]     			=   processor(text=UpperCAmelCase      )

      __lowerCAmelCase:     List[str]     			=   tokenizer(UpperCAmelCase    ,						return_token_type_ids=UpperCAmelCase    ,						add_special_tokens=UpperCAmelCase      )

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


     def 						UpperCAmelCase						(							self    :	Optional[Any]      )							->			Any:
      __lowerCAmelCase:     Optional[Any]     			=   self.get_image_processor()
      __lowerCAmelCase:     int     			=   self.get_tokenizer()

      __lowerCAmelCase:     Tuple     			=   PixaStructProcessor(tokenizer=UpperCAmelCase    ,						image_processor=UpperCAmelCase      )

      __lowerCAmelCase:     List[str]     			=   'lower newer'
      __lowerCAmelCase:     Tuple     			=   self.prepare_image_inputs()

      __lowerCAmelCase:     Dict     			=   processor(text=UpperCAmelCase    ,						images=UpperCAmelCase      )

      self.assertListEqual(
          list(inputs.keys()      )    ,						['flattened_patches', 'attention_mask', 'decoder_attention_mask', 'decoder_input_ids']      )

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


     def 						UpperCAmelCase						(							self    :	Any      )							->			Dict:
      __lowerCAmelCase:     List[str]     			=   self.get_image_processor()
      __lowerCAmelCase:     Optional[Any]     			=   self.get_tokenizer()

      __lowerCAmelCase:     List[str]     			=   PixaStructProcessor(tokenizer=UpperCAmelCase    ,						image_processor=UpperCAmelCase      )

      __lowerCAmelCase:     str     			=   'lower newer'
      __lowerCAmelCase:     List[str]     			=   self.prepare_image_inputs()

      __lowerCAmelCase:     int     			=   processor(text=UpperCAmelCase    ,						images=UpperCAmelCase      )

      __lowerCAmelCase:     Dict     			=   [5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
      __lowerCAmelCase:     int     			=   [7_7_0, 7_7_0, 7_7_0, 7_7_0]
      # with text
      for i, max_patch in enumerate(UpperCAmelCase      ):
       __lowerCAmelCase:     Dict     			=   processor(text=UpperCAmelCase    ,						images=UpperCAmelCase    ,						max_patches=UpperCAmelCase      )
       self.assertEqual(inputs['flattened_patches'][0].shape[0]    ,						UpperCAmelCase      )
       self.assertEqual(inputs['flattened_patches'][0].shape[1]    ,						expected_hidden_size[i]      )

      # without text input
      for i, max_patch in enumerate(UpperCAmelCase      ):
       __lowerCAmelCase:     List[str]     			=   processor(images=UpperCAmelCase    ,						max_patches=UpperCAmelCase      )
       self.assertEqual(inputs['flattened_patches'][0].shape[0]    ,						UpperCAmelCase      )
       self.assertEqual(inputs['flattened_patches'][0].shape[1]    ,						expected_hidden_size[i]      )


     def 						UpperCAmelCase						(							self    :	Dict      )							->			Optional[int]:
      __lowerCAmelCase:     Optional[int]     			=   self.get_image_processor()
      __lowerCAmelCase:     int     			=   self.get_tokenizer()

      __lowerCAmelCase:     int     			=   PixaStructProcessor(tokenizer=UpperCAmelCase    ,						image_processor=UpperCAmelCase      )

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

      __lowerCAmelCase:     Optional[Any]     			=   processor.batch_decode(UpperCAmelCase      )
      __lowerCAmelCase:     Dict     			=   tokenizer.batch_decode(UpperCAmelCase      )

      self.assertListEqual(UpperCAmelCase    ,						UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	Any      )							->			Dict:
      __lowerCAmelCase:     Tuple     			=   self.get_image_processor()
      __lowerCAmelCase:     List[str]     			=   self.get_tokenizer()

      __lowerCAmelCase:     str     			=   PixaStructProcessor(tokenizer=UpperCAmelCase    ,						image_processor=UpperCAmelCase      )

      __lowerCAmelCase:     List[Any]     			=   'lower newer'
      __lowerCAmelCase:     List[str]     			=   self.prepare_image_inputs()

      __lowerCAmelCase:     Union[str, Any]     			=   processor(text=UpperCAmelCase    ,						images=UpperCAmelCase      )

      # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
      self.assertListEqual(
          list(inputs.keys()      )    ,						['flattened_patches', 'attention_mask', 'decoder_attention_mask', 'decoder_input_ids']      )

      __lowerCAmelCase:     Dict     			=   processor(text=UpperCAmelCase      )

      # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
      self.assertListEqual(list(inputs.keys()      )    ,						['input_ids', 'attention_mask']      )








from math import floor, sqrt







def _a				( SCREAMING_SNAKE_CASE	:    int	)					->					int:

 """simple docstring"""






 __lowerCAmelCase:     Optional[int]     			=   0.0
 __lowerCAmelCase:     Any     			=   1.0
 __lowerCAmelCase:     List[str]     			=   int(sqrt(SCREAMING_SNAKE_CASE	)	)
 __lowerCAmelCase:     Union[str, Any]     			=   root
 __lowerCAmelCase:     List[str]     			=   0
 while integer_part != 2 * root:
  __lowerCAmelCase:     List[str]     			=   denominator * integer_part - numerator
  __lowerCAmelCase:     Optional[int]     			=   (n - numerator**2) / denominator
  __lowerCAmelCase:     Union[str, Any]     			=   int((root + numerator) / denominator	)
  period += 1
 return period







def _a				( SCREAMING_SNAKE_CASE	:    int = 1_00_00	)					->					int:

 """simple docstring"""






 __lowerCAmelCase:     Optional[int]     			=   0
 for i in range(2					,      n + 1	):
  __lowerCAmelCase:     List[str]     			=   sqrt(SCREAMING_SNAKE_CASE	)
  if sr - floor(SCREAMING_SNAKE_CASE	) != 0 and continuous_fraction_period(SCREAMING_SNAKE_CASE	) % 2 == 1:
   count_odd_periods += 1
 return count_odd_periods


if __name__ == "__main__":
  print(f"{solution(int(input().strip()))}")





