

import math as m






def    __lowercase (       _A  ,      _A  ,      _A=0.001     )       ->  Optional[Any]:
      return (f(a + h     ) - f(a - h     )) / (2 * h)






def    __lowercase (       _A  ,      _A=0  ,      _A=100  ,      _A=0.0001  ,      _A=1e-6  ,      _A=False     )       ->  Tuple:
      SCREAMING_SNAKE_CASE	: List[Any]					       =				xa  # set the initial guess
      SCREAMING_SNAKE_CASE	: Tuple					       =				[a]
      SCREAMING_SNAKE_CASE	: Optional[int]					       =				abs(f(_A     )     )
      SCREAMING_SNAKE_CASE	: str					       =				lambda _A     : calc_derivative(_A  ,      _A  ,      h=_A     )  # noqa: E731  Derivative of f(x)
      for _ in range(_A     ):
            if fa(_A     ) == 0:
                  raise ValueError("""No converging solution found"""     )
            SCREAMING_SNAKE_CASE	: Dict					       =				a - f(_A     ) / fa(_A     )  # Calculate the next estimate
            if logsteps:
                  steps.append(_A     )
            if error < maxerror:
                  break
      else:
            raise ValueError("""Iteration limit reached, no converging solution found"""     )
      if logsteps:
            # If logstep is true, then log intermediate steps
            return a, error, steps
      return a, error


if __name__ == "__main__":
  from matplotlib import pyplot as plt

  UpperCAmelCase__	:       List[str]			      =      lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
  UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__	:       str			      =      newton_raphson(
      f, xa=10, maxiter=1_000, step=1E-6, logsteps=True
  )
  plt.plot([abs(f(x)) for x in steps])
  plt.xlabel("""step""")
  plt.ylabel("""error""")
  plt.show()
  print(F"""solution = {{{solution:f}}}, error = {{{error:f}}}""")







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     a__    :







  """simple docstring"""




  def __init__(     self      :	Any			,			UpperCAmelCase__      :	Union[str, Any]			,			UpperCAmelCase__      :	List[str]=1_3			,			UpperCAmelCase__      :	Any=3_2			,			UpperCAmelCase__      :	Union[str, Any]=3			,			UpperCAmelCase__      :	List[str]=[3, 3, 5]			,			UpperCAmelCase__      :	int=[3_2, 1_6, 2_4]			,			UpperCAmelCase__      :	Dict=[1_6, 2_4, 4_0]			,			UpperCAmelCase__      :	Optional[Any]=[1, 1, 2]			,			UpperCAmelCase__      :	List[Any]=[1, 1, 2]			,			UpperCAmelCase__      :	str=[1, 6, 6]			,			UpperCAmelCase__      :	List[Any]=True			,			UpperCAmelCase__      :	int=True			,			UpperCAmelCase__      :	Any=3_7			,			UpperCAmelCase__      :	Any="gelu"			,			UpperCAmelCase__      :	List[Any]=1_0			,			)   ->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[int]					       =				parent
        SCREAMING_SNAKE_CASE	: Any					       =				batch_size
        SCREAMING_SNAKE_CASE	: List[Any]					       =				image_size
        SCREAMING_SNAKE_CASE	: str					       =				num_channels
        SCREAMING_SNAKE_CASE	: int					       =				kernel_sizes
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				in_channels
        SCREAMING_SNAKE_CASE	: List[str]					       =				out_channels
        SCREAMING_SNAKE_CASE	: int					       =				strides
        SCREAMING_SNAKE_CASE	: List[str]					       =				num_block_repeats
        SCREAMING_SNAKE_CASE	: Tuple					       =				expand_ratios
        SCREAMING_SNAKE_CASE	: Dict					       =				is_training
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				hidden_act
        SCREAMING_SNAKE_CASE	: int					       =				num_labels
        SCREAMING_SNAKE_CASE	: Dict					       =				use_labels


  def 	_lowercase (     self      :	Any						)   ->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

        SCREAMING_SNAKE_CASE	: Dict					       =				self.get_config()
        return config, pixel_values, labels


  def 	_lowercase (     self      :	Tuple						)   ->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 	_lowercase (     self      :	List[Any]			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	int			,			UpperCAmelCase__      :	Optional[Any]						)   ->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: List[Any]					       =				EfficientNetModel(config=UpperCAmelCase__						)
        model.to(UpperCAmelCase__						)
        model.eval()
        SCREAMING_SNAKE_CASE	: str					       =				model(UpperCAmelCase__						)
        # 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 	_lowercase (     self      :	Optional[int]			,			UpperCAmelCase__      :	List[Any]			,			UpperCAmelCase__      :	Optional[int]			,			UpperCAmelCase__      :	Union[str, Any]						)   ->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				EfficientNetForImageClassification(UpperCAmelCase__						)
        model.to(UpperCAmelCase__						)
        model.eval()
        SCREAMING_SNAKE_CASE	: Any					       =				model(UpperCAmelCase__			,			labels=UpperCAmelCase__						)
        self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_labels)						)




  def 	_lowercase (     self      :	Dict						)   ->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: List[str]					       =				self.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Any					       =				config_and_inputs
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				{"""pixel_values""": pixel_values}
        return config, inputs_dict







@require_torch
class     a__    (				UpperCAmelCase		,     UpperCAmelCase		,     unittest.TestCase    ):







  """simple docstring"""




  UpperCAmelCase__ :	Tuple         =(EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
  UpperCAmelCase__ :	Optional[int]         =(
      {"""feature-extraction""": EfficientNetModel, """image-classification""": EfficientNetForImageClassification}
      if is_torch_available()
      else {}
  )

  UpperCAmelCase__ :	List[str]         =False
  UpperCAmelCase__ :	Any         =False
  UpperCAmelCase__ :	Union[str, Any]         =False
  UpperCAmelCase__ :	Dict         =False
  UpperCAmelCase__ :	List[str]         =False


  def 	_lowercase (     self      :	Optional[Any]						)   ->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				EfficientNetModelTester(self						)
        SCREAMING_SNAKE_CASE	: Any					       =				ConfigTester(
            self			,			config_class=UpperCAmelCase__			,			has_text_modality=UpperCAmelCase__			,			hidden_size=3_7						)


  def 	_lowercase (     self      :	Optional[int]						)   ->List[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 	_lowercase (     self      :	List[Any]						)   ->Optional[int]:
        """simple docstring"""







        return


  @unittest.skip(reason="""EfficientNet does not use inputs_embeds"""						)
  def 	_lowercase (     self      :	Dict						)   ->Tuple:
        """simple docstring"""







        pass


  @unittest.skip(reason="""EfficientNet does not support input and output embeddings"""						)
  def 	_lowercase (     self      :	Dict						)   ->Dict:
        """simple docstring"""







        pass


  @unittest.skip(reason="""EfficientNet does not use feedforward chunking"""						)
  def 	_lowercase (     self      :	Optional[Any]						)   ->Any:
        """simple docstring"""







        pass


  def 	_lowercase (     self      :	str						)   ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Optional[Any]					       =				self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE	: str					       =				model_class(UpperCAmelCase__						)
              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				inspect.signature(model.forward						)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              SCREAMING_SNAKE_CASE	: List[str]					       =				[*signature.parameters.keys()]

              SCREAMING_SNAKE_CASE	: Dict					       =				["""pixel_values"""]
              self.assertListEqual(arg_names[:1]			,			UpperCAmelCase__						)


  def 	_lowercase (     self      :	Optional[int]						)   ->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[int]					       =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase__						)


  def 	_lowercase (     self      :	List[Any]						)   ->int:
        """simple docstring"""







        def check_hidden_states_output(UpperCAmelCase__      :	Optional[Any]			,			UpperCAmelCase__      :	Any			,			UpperCAmelCase__      :	Dict						):
              SCREAMING_SNAKE_CASE	: Optional[Any]					       =				model_class(UpperCAmelCase__						)
              model.to(UpperCAmelCase__						)
              model.eval()

              with torch.no_grad():
                    SCREAMING_SNAKE_CASE	: Any					       =				model(**self._prepare_for_class(UpperCAmelCase__			,			UpperCAmelCase__						)						)

              SCREAMING_SNAKE_CASE	: Optional[int]					       =				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
              SCREAMING_SNAKE_CASE	: Any					       =				sum(config.num_block_repeats						) * 4
              self.assertEqual(len(UpperCAmelCase__						)			,			UpperCAmelCase__						)

              # 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]			,			)

        SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Tuple					       =				self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE	: Optional[Any]					       =				True
              check_hidden_states_output(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__						)

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

              check_hidden_states_output(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__						)


  def 	_lowercase (     self      :	Union[str, Any]						)   ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Tuple					       =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__						)


  @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""						)
  def 	_lowercase (     self      :	List[str]						)   ->Optional[Any]:
        """simple docstring"""







        pass


  @slow
  def 	_lowercase (     self      :	Dict						)   ->List[Any]:
        """simple docstring"""







        for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE	: str					       =				EfficientNetModel.from_pretrained(UpperCAmelCase__						)
              self.assertIsNotNone(UpperCAmelCase__						)




  @is_pipeline_test
  @require_vision
  @slow
  def 	_lowercase (     self      :	Any						)   ->Any:
        """simple docstring"""







        super().test_pipeline_image_classification()






def    __lowercase (       )       ->  List[Any]:
      SCREAMING_SNAKE_CASE	: List[str]					       =				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
      return image







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







  """simple docstring"""




  @cached_property
  def 	_lowercase (     self      :	List[str]						)   ->Tuple:
        """simple docstring"""







        return AutoImageProcessor.from_pretrained("""google/efficientnet-b7"""						) if is_vision_available() else None




  @slow
  def 	_lowercase (     self      :	List[Any]						)   ->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Tuple					       =				EfficientNetForImageClassification.from_pretrained("""google/efficientnet-b7"""						).to(UpperCAmelCase__						)

        SCREAMING_SNAKE_CASE	: int					       =				self.default_image_processor
        SCREAMING_SNAKE_CASE	: List[Any]					       =				prepare_img()
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				image_processor(images=UpperCAmelCase__			,			return_tensors="""pt"""						).to(UpperCAmelCase__						)

        # forward pass
        with torch.no_grad():
              SCREAMING_SNAKE_CASE	: Optional[Any]					       =				model(**UpperCAmelCase__						)

        # verify the logits
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				torch.Size((1, 1_0_0_0)						)
        self.assertEqual(outputs.logits.shape			,			UpperCAmelCase__						)

        SCREAMING_SNAKE_CASE	: Any					       =				torch.tensor([0.00_01, 0.00_02, 0.00_02]						).to(UpperCAmelCase__						)
        self.assertTrue(torch.allclose(outputs.logits[0, :3]			,			UpperCAmelCase__			,			atol=1e-4						)						)




