
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 						__magic_name__				:





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





		def  _A							(							self      :		Dict			):
							lowerCAmelCase						:   List[Any]							=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

							lowerCAmelCase						:   Any							=       None
							if self.use_labels:
												lowerCAmelCase						:   Any							=       ids_tensor([self.batch_size]  ,					self.num_labels			)

							lowerCAmelCase						:   List[str]							=       self.get_config()

							return config, pixel_values, labels





		def  _A							(							self      :		str			):
							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  _A							(							self      :		Optional[int]  ,					lowerCamelCase__      :		List[Any]  ,					lowerCamelCase__      :		Dict  ,					lowerCamelCase__      :		Any			):
							lowerCAmelCase						:   List[str]							=       ResNetModel(config=lowerCamelCase__			)
							model.to(lowerCamelCase__			)
							model.eval()
							lowerCAmelCase						:   Optional[Any]							=       model(lowerCamelCase__			)
							# 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  _A							(							self      :		List[str]  ,					lowerCamelCase__      :		int  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		List[str]			):
							lowerCAmelCase						:   Union[str, Any]							=       self.num_labels
							lowerCAmelCase						:   str							=       ResNetForImageClassification(lowerCamelCase__			)
							model.to(lowerCamelCase__			)
							model.eval()
							lowerCAmelCase						:   Tuple							=       model(lowerCamelCase__  ,					labels=lowerCamelCase__			)
							self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.num_labels)			)





		def  _A							(							self      :		Dict  ,					lowerCamelCase__      :		Tuple  ,					lowerCamelCase__      :		Any  ,					lowerCamelCase__      :		int			):
							lowerCAmelCase						:   Any							=       ResNetBackbone(config=lowerCamelCase__			)
							model.to(lowerCamelCase__			)
							model.eval()
							lowerCAmelCase						:   Optional[Any]							=       model(lowerCamelCase__			)

							# 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						:   List[Any]							=       None
							lowerCAmelCase						:   int							=       ResNetBackbone(config=lowerCamelCase__			)
							model.to(lowerCamelCase__			)
							model.eval()
							lowerCAmelCase						:   Tuple							=       model(lowerCamelCase__			)

							# 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  _A							(							self      :		str			):
							lowerCAmelCase						:   Dict							=       self.prepare_config_and_inputs()
							lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase						:   int							=       config_and_inputs
							lowerCAmelCase						:   int							=       {'''pixel_values''': pixel_values}
							return config, inputs_dict



@require_torch
class 						__magic_name__				(  snake_case,			snake_case,			unittest.TestCase      ):
		_lowerCAmelCase						=		(
		    (
		        ResNetModel,
		        ResNetForImageClassification,
		        ResNetBackbone,
		    )
		    if is_torch_available()
		    else ()
		)
		_lowerCAmelCase						=		(
		    {"feature-extraction": ResNetModel, "image-classification": ResNetForImageClassification}
		    if is_torch_available()
		    else {}
		)

		_lowerCAmelCase						=		True
		_lowerCAmelCase						=		False
		_lowerCAmelCase						=		False
		_lowerCAmelCase						=		False
		_lowerCAmelCase						=		False





		def  _A							(							self      :		Union[str, Any]			):
							lowerCAmelCase						:   Optional[int]							=       ResNetModelTester(self			)
							lowerCAmelCase						:   Tuple							=       ConfigTester(self  ,					config_class=lowerCamelCase__  ,					has_text_modality=lowerCamelCase__			)





		def  _A							(							self      :		str			):
							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  _A							(							self      :		Union[str, Any]			):
							return





		@unittest.skip(reason='''ResNet does not use inputs_embeds'''			)
		def  _A							(							self      :		int			):
							pass





		@unittest.skip(reason='''ResNet does not support input and output embeddings'''			)
		def  _A							(							self      :		str			):
							pass





		def  _A							(							self      :		Tuple			):
							lowerCAmelCase		,							lowerCAmelCase						:   Optional[Any]							=       self.model_tester.prepare_config_and_inputs_for_common()

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

												lowerCAmelCase						:   str							=       ['''pixel_values''']
												self.assertListEqual(arg_names[:1]  ,					lowerCamelCase__			)





		def  _A							(							self      :		List[Any]			):
							lowerCAmelCase						:   int							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase__			)





		def  _A							(							self      :		str			):
							lowerCAmelCase						:   Any							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*lowerCamelCase__			)





		def  _A							(							self      :		List[str]			):
							lowerCAmelCase		,							lowerCAmelCase						:   Optional[Any]							=       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												lowerCAmelCase						:   Tuple							=       model_class(config=lowerCamelCase__			)
												for name, module in model.named_modules():
																	if isinstance(lowerCamelCase__  ,					(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  _A							(							self      :		str			):
							def check_hidden_states_output(lowerCamelCase__      :		Tuple  ,					lowerCamelCase__      :		Dict  ,					lowerCamelCase__      :		List[str]			):
												lowerCAmelCase						:   Optional[int]							=       model_class(lowerCamelCase__			)
												model.to(lowerCamelCase__			)
												model.eval()

												with torch.no_grad():
																	lowerCAmelCase						:   List[Any]							=       model(**self._prepare_for_class(lowerCamelCase__  ,					lowerCamelCase__			)			)

												lowerCAmelCase						:   List[str]							=       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

												lowerCAmelCase						:   Any							=       self.model_tester.num_stages
												self.assertEqual(len(lowerCamelCase__			)  ,					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		,							lowerCAmelCase						:   Any							=       self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase						:   int							=       ['''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(lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__			)

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	lowerCAmelCase						:   Optional[Any]							=       True

																	check_hidden_states_output(lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__			)





		@unittest.skip(reason='''ResNet does not use feedforward chunking'''			)
		def  _A							(							self      :		List[str]			):
							pass





		def  _A							(							self      :		Union[str, Any]			):
							lowerCAmelCase						:   int							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__			)





		@slow
		def  _A							(							self      :		str			):
							for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowerCAmelCase						:   str							=       ResNetModel.from_pretrained(lowerCamelCase__			)
												self.assertIsNotNone(lowerCamelCase__			)



def       UpperCAmelCase__							(    ):


					'''simple docstring'''

					lowerCAmelCase						:   str							=       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
					return image


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





		@cached_property
		def  _A							(							self      :		Tuple			):
							return (
							    AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
							    if is_vision_available()
							    else None
							)





		@slow
		def  _A							(							self      :		Any			):
							lowerCAmelCase						:   Optional[int]							=       ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			).to(lowerCamelCase__			)

							lowerCAmelCase						:   str							=       self.default_image_processor
							lowerCAmelCase						:   Optional[int]							=       prepare_img()
							lowerCAmelCase						:   List[str]							=       image_processor(images=lowerCamelCase__  ,					return_tensors='''pt'''			).to(lowerCamelCase__			)

							# forward pass
							with torch.no_grad():
												lowerCAmelCase						:   int							=       model(**lowerCamelCase__			)

							# verify the logits
							lowerCAmelCase						:   Optional[Any]							=       torch.Size((1, 1_0_0_0)			)
							self.assertEqual(outputs.logits.shape  ,					lowerCamelCase__			)

							lowerCAmelCase						:   Any							=       torch.tensor([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]			).to(lowerCamelCase__			)

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



@require_torch
class 						__magic_name__				(  snake_case,			unittest.TestCase      ):
		_lowerCAmelCase						=		(ResNetBackbone,) if is_torch_available() else ()
		_lowerCAmelCase						=		False
		_lowerCAmelCase						=		ResNetConfig





		def  _A							(							self      :		List[str]			):
							lowerCAmelCase						:   Any							=       ResNetModelTester(self			)


from math import floor, sqrt
def       UpperCAmelCase__							(    __magic_name__  :       int					):


					'''simple docstring'''

					lowerCAmelCase						:   int							=       0.0
					lowerCAmelCase						:   int							=       1.0
					lowerCAmelCase						:   Any							=       int(sqrt(__magic_name__					)					)
					lowerCAmelCase						:   Optional[int]							=       root
					lowerCAmelCase						:   Dict							=       0
					while integer_part != 2 * root:
										lowerCAmelCase						:   str							=       denominator * integer_part - numerator
										lowerCAmelCase						:   Dict							=       (n - numerator**2) / denominator
										lowerCAmelCase						:   str							=       int((root + numerator) / denominator					)
										period += 1
					return period
def       UpperCAmelCase__							(    __magic_name__  :       int = 1_00_00					):


					'''simple docstring'''

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


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