






import inspect
import unittest

from transformers import ResNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
  from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor






class 							A_				:


     def __init__(							self    :	Union[str, Any]    ,						UpperCAmelCase    :	Any    ,						UpperCAmelCase    :	Dict=3    ,						UpperCAmelCase    :	Optional[int]=3_2    ,						UpperCAmelCase    :	Optional[Any]=3    ,						UpperCAmelCase    :	int=1_0    ,						UpperCAmelCase    :	str=[1_0, 2_0, 3_0, 4_0]    ,						UpperCAmelCase    :	List[Any]=[1, 1, 2, 1]    ,						UpperCAmelCase    :	int=True    ,						UpperCAmelCase    :	int=True    ,						UpperCAmelCase    :	str="relu"    ,						UpperCAmelCase    :	Optional[Any]=3    ,						UpperCAmelCase    :	Optional[Any]=None    ,						UpperCAmelCase    :	Any=["stage2", "stage3", "stage4"]    ,						UpperCAmelCase    :	List[Any]=[2, 3, 4]    ,						)							->			Dict:
      __lowerCAmelCase:     Optional[Any]     			=   parent
      __lowerCAmelCase:     str     			=   batch_size
      __lowerCAmelCase:     List[str]     			=   image_size
      __lowerCAmelCase:     Dict     			=   num_channels
      __lowerCAmelCase:     int     			=   embeddings_size
      __lowerCAmelCase:     Any     			=   hidden_sizes
      __lowerCAmelCase:     Union[str, Any]     			=   depths
      __lowerCAmelCase:     List[Any]     			=   is_training
      __lowerCAmelCase:     Dict     			=   use_labels
      __lowerCAmelCase:     Any     			=   hidden_act
      __lowerCAmelCase:     Tuple     			=   num_labels
      __lowerCAmelCase:     List[Any]     			=   scope
      __lowerCAmelCase:     Union[str, Any]     			=   len(UpperCAmelCase      )
      __lowerCAmelCase:     List[Any]     			=   out_features
      __lowerCAmelCase:     List[Any]     			=   out_indices


     def 						UpperCAmelCase						(							self    :	List[str]      )							->			Dict:
      __lowerCAmelCase:     str     			=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

      __lowerCAmelCase:     Dict     			=   None
      if self.use_labels:
       __lowerCAmelCase:     List[str]     			=   ids_tensor([self.batch_size]    ,						self.num_labels      )

      __lowerCAmelCase:     Optional[Any]     			=   self.get_config()

      return config, pixel_values, labels


     def 						UpperCAmelCase						(							self    :	List[str]      )							->			Tuple:
      return ResNetConfig(
          num_channels=self.num_channels    ,						embeddings_size=self.embeddings_size    ,						hidden_sizes=self.hidden_sizes    ,						depths=self.depths    ,						hidden_act=self.hidden_act    ,						num_labels=self.num_labels    ,						out_features=self.out_features    ,						out_indices=self.out_indices    ,						)


     def 						UpperCAmelCase						(							self    :	Optional[int]    ,						UpperCAmelCase    :	Tuple    ,						UpperCAmelCase    :	Any    ,						UpperCAmelCase    :	str      )							->			Union[str, Any]:
      __lowerCAmelCase:     Any     			=   ResNetModel(config=UpperCAmelCase      )
      model.to(UpperCAmelCase      )
      model.eval()
      __lowerCAmelCase:     Tuple     			=   model(UpperCAmelCase      )
      # expected last hidden states: B, C, H // 32, W // 32
      self.parent.assertEqual(
          result.last_hidden_state.shape    ,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)    ,						)


     def 						UpperCAmelCase						(							self    :	Optional[int]    ,						UpperCAmelCase    :	List[str]    ,						UpperCAmelCase    :	Optional[int]    ,						UpperCAmelCase    :	Optional[Any]      )							->			List[str]:
      __lowerCAmelCase:     Optional[Any]     			=   self.num_labels
      __lowerCAmelCase:     int     			=   ResNetForImageClassification(UpperCAmelCase      )
      model.to(UpperCAmelCase      )
      model.eval()
      __lowerCAmelCase:     Dict     			=   model(UpperCAmelCase    ,						labels=UpperCAmelCase      )
      self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_labels)      )


     def 						UpperCAmelCase						(							self    :	Union[str, Any]    ,						UpperCAmelCase    :	List[str]    ,						UpperCAmelCase    :	Optional[Any]    ,						UpperCAmelCase    :	Union[str, Any]      )							->			Tuple:
      __lowerCAmelCase:     Any     			=   ResNetBackbone(config=UpperCAmelCase      )
      model.to(UpperCAmelCase      )
      model.eval()
      __lowerCAmelCase:     List[str]     			=   model(UpperCAmelCase      )

      # verify feature maps
      self.parent.assertEqual(len(result.feature_maps      )    ,						len(config.out_features      )      )
      self.parent.assertListEqual(list(result.feature_maps[0].shape      )    ,						[self.batch_size, self.hidden_sizes[1], 4, 4]      )

      # verify channels
      self.parent.assertEqual(len(model.channels      )    ,						len(config.out_features      )      )
      self.parent.assertListEqual(model.channels    ,						config.hidden_sizes[1:]      )

      # verify backbone works with out_features=None
      __lowerCAmelCase:     Any     			=   None
      __lowerCAmelCase:     Union[str, Any]     			=   ResNetBackbone(config=UpperCAmelCase      )
      model.to(UpperCAmelCase      )
      model.eval()
      __lowerCAmelCase:     str     			=   model(UpperCAmelCase      )

      # verify feature maps
      self.parent.assertEqual(len(result.feature_maps      )    ,						1      )
      self.parent.assertListEqual(list(result.feature_maps[0].shape      )    ,						[self.batch_size, self.hidden_sizes[-1], 1, 1]      )

      # verify channels
      self.parent.assertEqual(len(model.channels      )    ,						1      )
      self.parent.assertListEqual(model.channels    ,						[config.hidden_sizes[-1]]      )


     def 						UpperCAmelCase						(							self    :	Union[str, Any]      )							->			Optional[Any]:
      __lowerCAmelCase:     Union[str, Any]     			=   self.prepare_config_and_inputs()
      __lowerCAmelCase:     Tuple     			=   config_and_inputs
      __lowerCAmelCase:     List[Any]     			=   {'''pixel_values''': pixel_values}
      return config, inputs_dict







@require_torch
class 							A_				(	A_							,			A_							,			unittest.TestCase      ):
     _lowercase    :  Dict          =			(
         (
             ResNetModel,
             ResNetForImageClassification,
             ResNetBackbone,
         )
         if is_torch_available()
         else ()
     )
     _lowercase    :  List[Any]          =			(
         {'feature-extraction': ResNetModel, 'image-classification': ResNetForImageClassification}
         if is_torch_available()
         else {}
     )

     _lowercase    :  Dict          =			True
     _lowercase    :  str          =			False
     _lowercase    :  Optional[int]          =			False
     _lowercase    :  List[str]          =			False
     _lowercase    :  Tuple          =			False


     def 						UpperCAmelCase						(							self    :	int      )							->			List[str]:
      __lowerCAmelCase:     Union[str, Any]     			=   ResNetModelTester(self      )
      __lowerCAmelCase:     List[Any]     			=   ConfigTester(self    ,						config_class=UpperCAmelCase    ,						has_text_modality=UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	Tuple      )							->			int:
      self.create_and_test_config_common_properties()
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()


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


     @unittest.skip(reason='ResNet does not use inputs_embeds'      )
     def 						UpperCAmelCase						(							self    :	Optional[int]      )							->			List[Any]:
      pass


     @unittest.skip(reason='ResNet does not support input and output embeddings'      )
     def 						UpperCAmelCase						(							self    :	Dict      )							->			Optional[Any]:
      pass


     def 						UpperCAmelCase						(							self    :	List[Any]      )							->			Optional[int]:
      __lowerCAmelCase:     Union[str, Any]     			=   self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __lowerCAmelCase:     Optional[Any]     			=   model_class(UpperCAmelCase      )
       __lowerCAmelCase:     List[Any]     			=   inspect.signature(model.forward      )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       __lowerCAmelCase:     Any     			=   [*signature.parameters.keys()]

       __lowerCAmelCase:     Any     			=   ['''pixel_values''']
       self.assertListEqual(arg_names[:1]    ,						UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	Any      )							->			Union[str, Any]:
      __lowerCAmelCase:     List[str]     			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	Any      )							->			Optional[int]:
      __lowerCAmelCase:     Any     			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_backbone(*UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	Tuple      )							->			str:
      __lowerCAmelCase:     int     			=   self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __lowerCAmelCase:     Union[str, Any]     			=   model_class(config=UpperCAmelCase      )
       for name, module in model.named_modules():
        if isinstance(UpperCAmelCase    ,						(nn.BatchNormad, nn.GroupNorm)      ):
         self.assertTrue(
             torch.all(module.weight == 1      )    ,						msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''    ,						)
         self.assertTrue(
             torch.all(module.bias == 0      )    ,						msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''    ,						)


     def 						UpperCAmelCase						(							self    :	int      )							->			Any:



      def check_hidden_states_output(UpperCAmelCase    :	Union[str, Any]    ,						UpperCAmelCase    :	List[Any]    ,						UpperCAmelCase    :	Union[str, Any]      ):
       __lowerCAmelCase:     str     			=   model_class(UpperCAmelCase      )
       model.to(UpperCAmelCase      )
       model.eval()

       with torch.no_grad():
        __lowerCAmelCase:     Any     			=   model(**self._prepare_for_class(UpperCAmelCase    ,						UpperCAmelCase      )      )

       __lowerCAmelCase:     Optional[int]     			=   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

       __lowerCAmelCase:     Dict     			=   self.model_tester.num_stages
       self.assertEqual(len(UpperCAmelCase      )    ,						expected_num_stages + 1      )

       # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
       self.assertListEqual(
           list(hidden_states[0].shape[-2:]      )    ,						[self.model_tester.image_size // 4, self.model_tester.image_size // 4]    ,						)

      __lowerCAmelCase:     Union[str, Any]     			=   self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCAmelCase:     Union[str, Any]     			=   ['''basic''', '''bottleneck''']
      for model_class in self.all_model_classes:
       for layer_type in layers_type:
        __lowerCAmelCase:     List[Any]     			=   layer_type
        __lowerCAmelCase:     Union[str, Any]     			=   True
        check_hidden_states_output(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      )

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        __lowerCAmelCase:     Dict     			=   True

        check_hidden_states_output(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      )


     @unittest.skip(reason='ResNet does not use feedforward chunking'      )
     def 						UpperCAmelCase						(							self    :	Dict      )							->			List[Any]:
      pass


     def 						UpperCAmelCase						(							self    :	Optional[Any]      )							->			List[Any]:
      __lowerCAmelCase:     Optional[Any]     			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase      )


     @slow
     def 						UpperCAmelCase						(							self    :	List[str]      )							->			Union[str, Any]:



      for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __lowerCAmelCase:     List[str]     			=   ResNetModel.from_pretrained(UpperCAmelCase      )
       self.assertIsNotNone(UpperCAmelCase      )







def _a				( )					->					int:

 """simple docstring"""






 __lowerCAmelCase:     List[Any]     			=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
 return image






@require_torch
@require_vision
class 							A_				(	unittest.TestCase      ):


     @cached_property
     def 						UpperCAmelCase						(							self    :	List[str]      )							->			List[Any]:
      return (
          AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )
          if is_vision_available()
          else None
      )


     @slow
     def 						UpperCAmelCase						(							self    :	List[Any]      )							->			int:
      __lowerCAmelCase:     Any     			=   ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).to(UpperCAmelCase      )

      __lowerCAmelCase:     int     			=   self.default_image_processor
      __lowerCAmelCase:     Optional[Any]     			=   prepare_img()
      __lowerCAmelCase:     Optional[Any]     			=   image_processor(images=UpperCAmelCase    ,						return_tensors='pt'      ).to(UpperCAmelCase      )

      # forward pass
      with torch.no_grad():
       __lowerCAmelCase:     Optional[int]     			=   model(**UpperCAmelCase      )

      # verify the logits
      __lowerCAmelCase:     Optional[int]     			=   torch.Size((1, 1_0_0_0)      )
      self.assertEqual(outputs.logits.shape    ,						UpperCAmelCase      )

      __lowerCAmelCase:     Optional[int]     			=   torch.tensor([-11.1069, -9.7877, -8.3777]      ).to(UpperCAmelCase      )

      self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,						UpperCAmelCase    ,						atol=1E-4      )      )







@require_torch
class 							A_				(	A_							,			unittest.TestCase      ):
     _lowercase    :  Optional[int]          =			(ResNetBackbone,) if is_torch_available() else ()
     _lowercase    :  Optional[Any]          =			False
     _lowercase    :  int          =			ResNetConfig


     def 						UpperCAmelCase						(							self    :	Dict      )							->			Optional[Any]:
      __lowerCAmelCase:     Optional[int]     			=   ResNetModelTester(self      )








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()))}")





