

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() = }""")





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  )  )

