code: 
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	int = 1000			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			lowerCamelCase								=     0
  			lowerCamelCase								=     0
  			while 1:
  						num += 3
  						if num >= n:
  									break
  						total += num
  						num += 2
  						if num >= n:
  									break
  						total += num
  						num += 1
  						if num >= n:
  									break
  						total += num
  						num += 3
  						if num >= n:
  									break
  						total += num
  						num += 1
  						if num >= n:
  									break
  						total += num
  						num += 2
  						if num >= n:
  									break
  						total += num
  						num += 3
  						if num >= n:
  									break
  						total += num
  			return total
  
  
  if __name__ == "__main__":
  		print(f"""{solution() = }""")
  
  
style_context: 
  
  
  import inspect
  import unittest
  
  from transformers import EfficientNetConfig
  from transformers.testing_utils import is_pipeline_test, require_torch, require_vision, slow, torch_device
  from transformers.utils import cached_property, is_torch_available, is_vision_available
  
  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 transformers import EfficientNetForImageClassification, EfficientNetModel
  		from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  if is_vision_available():
  		from PIL import Image
  
  		from transformers import AutoImageProcessor
  
  
  
  
  
  
  
  class 					__lowercase					:
  
  
  
  		"""simple docstring"""
  
  		def __init__(   self		,			A		,			A=13		,			A=32		,			A=3		,			A=[3, 3, 5]		,			A=[32, 16, 24]		,			A=[16, 24, 40]		,			A=[1, 1, 2]		,			A=[1, 1, 2]		,			A=[1, 6, 6]		,			A=True		,			A=True		,			A=37		,			A="gelu"		,			A=10		,			)     ->   Dict:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     parent
  					lowerCamelCase								=     batch_size
  					lowerCamelCase								=     image_size
  					lowerCamelCase								=     num_channels
  					lowerCamelCase								=     kernel_sizes
  					lowerCamelCase								=     in_channels
  					lowerCamelCase								=     out_channels
  					lowerCamelCase								=     strides
  					lowerCamelCase								=     num_block_repeats
  					lowerCamelCase								=     expand_ratios
  					lowerCamelCase								=     is_training
  					lowerCamelCase								=     hidden_act
  					lowerCamelCase								=     num_labels
  					lowerCamelCase								=     use_labels
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Optional[Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )
  
  					lowerCamelCase								=     None
  					if self.use_labels:
  								lowerCamelCase								=     ids_tensor([self.batch_size]		,			self.num_labels  )
  
  					lowerCamelCase								=     self.get_config()
  					return config, pixel_values, labels
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Optional[int]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					return EfficientNetConfig(
  					    num_channels=self.num_channels		,			kernel_sizes=self.kernel_sizes		,			in_channels=self.in_channels		,			out_channels=self.out_channels		,			strides=self.strides		,			num_block_repeats=self.num_block_repeats		,			expand_ratios=self.expand_ratios		,			hidden_act=self.hidden_act		,			num_labels=self.num_labels		,			)
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A		,			A		,			A  )     ->   List[Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     EfficientNetModel(config=A  )
  					model.to(A  )
  					model.eval()
  					lowerCamelCase								=     model(A  )
  					# expected last hidden states: B, C, H // 4, W // 4
  					self.parent.assertEqual(
  					    result.last_hidden_state.shape		,			(self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4)		,			)
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A		,			A		,			A  )     ->   Optional[int]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     EfficientNetForImageClassification(A  )
  					model.to(A  )
  					model.eval()
  					lowerCamelCase								=     model(A		,			labels=A  )
  					self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.num_labels)  )
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     self.prepare_config_and_inputs()
  					lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase								=     config_and_inputs
  					lowerCamelCase								=     {"""pixel_values""": pixel_values}
  					return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class 					__lowercase					(       a_ ,       a_ ,       unittest.TestCase     ):
  
  
  
  		"""simple docstring"""
  
  		UpperCamelCase    :				str 					=		(EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
  		UpperCamelCase    :				List[Any] 					=		(
  		    {"feature-extraction": EfficientNetModel, "image-classification": EfficientNetForImageClassification}
  		    if is_torch_available()
  		    else {}
  		)
  
  		UpperCamelCase    :				Any 					=		False
  		UpperCamelCase    :				Dict 					=		False
  		UpperCamelCase    :				Optional[int] 					=		False
  		UpperCamelCase    :				str 					=		False
  		UpperCamelCase    :				Optional[Any] 					=		False
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   str:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     EfficientNetModelTester(self  )
  					lowerCamelCase								=     ConfigTester(
  					    self		,			config_class=A		,			has_text_modality=A		,			hidden_size=37  )
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Optional[Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					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]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					return
  
  
  
  
  
  
  
  		@unittest.skip(reason="""EfficientNet does not use inputs_embeds"""  )
  		def 				__A      (   self  )     ->   Optional[Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					pass
  
  
  
  
  
  
  
  		@unittest.skip(reason="""EfficientNet does not support input and output embeddings"""  )
  		def 				__A      (   self  )     ->   Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					pass
  
  
  
  
  
  
  
  		@unittest.skip(reason="""EfficientNet does not use feedforward chunking"""  )
  		def 				__A      (   self  )     ->   List[Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					pass
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Optional[int]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase		,		lowerCamelCase								=     self.model_tester.prepare_config_and_inputs_for_common()
  
  					for model_class in self.all_model_classes:
  								lowerCamelCase								=     model_class(A  )
  								lowerCamelCase								=     inspect.signature(model.forward  )
  								# signature.parameters is an OrderedDict => so arg_names order is deterministic
  								lowerCamelCase								=     [*signature.parameters.keys()]
  
  								lowerCamelCase								=     ["""pixel_values"""]
  								self.assertListEqual(arg_names[:1]		,			A  )
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Dict:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     self.model_tester.prepare_config_and_inputs()
  					self.model_tester.create_and_check_model(*A  )
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Tuple:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					def check_hidden_states_output(A		,			A		,			A  ):
  								lowerCamelCase								=     model_class(A  )
  								model.to(A  )
  								model.eval()
  
  								with torch.no_grad():
  											lowerCamelCase								=     model(**self._prepare_for_class(A		,			A  )  )
  
  								lowerCamelCase								=     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  								lowerCamelCase								=     sum(config.num_block_repeats  ) * 4
  								self.assertEqual(len(A  )		,			A  )
  
  								# EfficientNet'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 // 2, self.model_tester.image_size // 2]		,			)
  
  					lowerCamelCase		,		lowerCamelCase								=     self.model_tester.prepare_config_and_inputs_for_common()
  
  					for model_class in self.all_model_classes:
  								lowerCamelCase								=     True
  								check_hidden_states_output(A		,			A		,			A  )
  
  								# check that output_hidden_states also work using config
  								del inputs_dict["output_hidden_states"]
  								lowerCamelCase								=     True
  
  								check_hidden_states_output(A		,			A		,			A  )
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   int:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     self.model_tester.prepare_config_and_inputs()
  					self.model_tester.create_and_check_for_image_classification(*A  )
  
  
  
  
  
  
  
  		@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""  )
  		def 				__A      (   self  )     ->   Union[str, Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					pass
  
  
  
  
  
  
  
  		@slow
  		def 				__A      (   self  )     ->   Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  								lowerCamelCase								=     EfficientNetModel.from_pretrained(A  )
  								self.assertIsNotNone(A  )
  
  
  
  
  
  
  
  		@is_pipeline_test
  		@require_vision
  		@slow
  		def 				__A      (   self  )     ->   Optional[Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					super().test_pipeline_image_classification()
  
  
  
  
  
  
  def   __lowerCamelCase	(       ):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			lowerCamelCase								=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
  			return image
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 					__lowercase					(       unittest.TestCase     ):
  
  
  
  		"""simple docstring"""
  
  		@cached_property
  		def 				__A      (   self  )     ->   Dict:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					return AutoImageProcessor.from_pretrained("""google/efficientnet-b7"""  ) if is_vision_available() else None
  
  
  
  
  
  
  
  		@slow
  		def 				__A      (   self  )     ->   List[str]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     EfficientNetForImageClassification.from_pretrained("""google/efficientnet-b7"""  ).to(A  )
  
  					lowerCamelCase								=     self.default_image_processor
  					lowerCamelCase								=     prepare_img()
  					lowerCamelCase								=     image_processor(images=A		,			return_tensors="""pt"""  ).to(A  )
  
  					# forward pass
  					with torch.no_grad():
  								lowerCamelCase								=     model(**A  )
  
  					# verify the logits
  					lowerCamelCase								=     torch.Size((1, 10_00)  )
  					self.assertEqual(outputs.logits.shape		,			A  )
  
  					lowerCamelCase								=     torch.tensor([0.0001, 0.0002, 0.0002]  ).to(A  )
  					self.assertTrue(torch.allclose(outputs.logits[0, :3]		,			A		,			atol=1e-4  )  )
  
  