




"""simple docstring"""







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 							snake_case_ :







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_=1_3					,				lowerCamelCase_=3_2					,				lowerCamelCase_=3					,				lowerCamelCase_=[3, 3, 5]					,				lowerCamelCase_=[3_2, 1_6, 2_4]					,				lowerCamelCase_=[1_6, 2_4, 4_0]					,				lowerCamelCase_=[1, 1, 2]					,				lowerCamelCase_=[1, 1, 2]					,				lowerCamelCase_=[1, 6, 6]					,				lowerCamelCase_=True					,				lowerCamelCase_=True					,				lowerCamelCase_=3_7					,				lowerCamelCase_="gelu"					,				lowerCamelCase_=1_0					,				)							->      Any:
       UpperCamelCase										=  parent
       UpperCamelCase										=  batch_size
       UpperCamelCase										=  image_size
       UpperCamelCase										=  num_channels
       UpperCamelCase										=  kernel_sizes
       UpperCamelCase										=  in_channels
       UpperCamelCase										=  out_channels
       UpperCamelCase										=  strides
       UpperCamelCase										=  num_block_repeats
       UpperCamelCase										=  expand_ratios
       UpperCamelCase										=  is_training
       UpperCamelCase										=  hidden_act
       UpperCamelCase										=  num_labels
       UpperCamelCase										=  use_labels







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

       UpperCamelCase										=  self.get_config()
       return config, pixel_values, labels







   def      UpperCAmelCase__ (		self)							->      str:
       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      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      Tuple:
       UpperCamelCase										=  EfficientNetModel(config=lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()
       UpperCamelCase										=  model(lowerCamelCase_)
       # 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      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      Any:
       UpperCamelCase										=  EfficientNetForImageClassification(lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()
       UpperCamelCase										=  model(lowerCamelCase_					,				labels=lowerCamelCase_)
       self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_labels))







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       UpperCamelCase										=  self.prepare_config_and_inputs()
       UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase										=  config_and_inputs
       UpperCamelCase										=  {'''pixel_values''': pixel_values}
       return config, inputs_dict

@require_torch
class 							snake_case_ (				lowerCamelCase_  ,   lowerCamelCase_  ,   unittest.TestCase ):







   """simple docstring"""




   A_										=					(EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
   A_										=					(
       {'''feature-extraction''': EfficientNetModel, '''image-classification''': EfficientNetForImageClassification}
       if is_torch_available()
       else {}
   )

   A_										=					False
   A_										=					False
   A_										=					False
   A_										=					False
   A_										=					False







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       UpperCamelCase										=  EfficientNetModelTester(self)
       UpperCamelCase										=  ConfigTester(
           self					,				config_class=lowerCamelCase_					,				has_text_modality=lowerCamelCase_					,				hidden_size=3_7)







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       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)							->      Any:
       return







   @unittest.skip(reason='''EfficientNet does not use inputs_embeds''')
   def      UpperCAmelCase__ (		self)							->      Any:
       pass







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







   @unittest.skip(reason='''EfficientNet does not use feedforward chunking''')
   def      UpperCAmelCase__ (		self)							->      List[str]:
       pass







   def      UpperCAmelCase__ (		self)							->      int:
       UpperCamelCase					,  UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

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

           UpperCamelCase										=  ['''pixel_values''']
           self.assertListEqual(arg_names[:1]					,				lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      Union[str, Any]:
       UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      Optional[int]:
       def check_hidden_states_output(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_):
           UpperCamelCase										=  model_class(lowerCamelCase_)
           model.to(lowerCamelCase_)
           model.eval()

           with torch.no_grad():
               UpperCamelCase										=  model(**self._prepare_for_class(lowerCamelCase_					,				lowerCamelCase_))

           UpperCamelCase										=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
           UpperCamelCase										=  sum(config.num_block_repeats) * 4
           self.assertEqual(len(lowerCamelCase_)					,				lowerCamelCase_)

           # 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]					,				)

       UpperCamelCase					,  UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCamelCase										=  True
           check_hidden_states_output(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           UpperCamelCase										=  True

           check_hidden_states_output(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_)







   @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
   def      UpperCAmelCase__ (		self)							->      List[Any]:
       pass







   @slow
   def      UpperCAmelCase__ (		self)							->      List[str]:
       for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase										=  EfficientNetModel.from_pretrained(lowerCamelCase_)
           self.assertIsNotNone(lowerCamelCase_)







   @is_pipeline_test
   @require_vision
   @slow
   def      UpperCAmelCase__ (		self)							->      Tuple:
       super().test_pipeline_image_classification()





def     __snake_case   (							):


    """simple docstring"""







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


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







   """simple docstring"""




   @cached_property
   def      UpperCAmelCase__ (		self)							->      Union[str, Any]:
       return AutoImageProcessor.from_pretrained('''google/efficientnet-b7''') if is_vision_available() else None







   @slow
   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  EfficientNetForImageClassification.from_pretrained('''google/efficientnet-b7''').to(lowerCamelCase_)

       UpperCamelCase										=  self.default_image_processor
       UpperCamelCase										=  prepare_img()
       UpperCamelCase										=  image_processor(images=lowerCamelCase_					,				return_tensors='''pt''').to(lowerCamelCase_)

       # forward pass
       with torch.no_grad():
           UpperCamelCase										=  model(**lowerCamelCase_)

       # verify the logits
       UpperCamelCase										=  torch.Size((1, 1_0_0_0))
       self.assertEqual(outputs.logits.shape					,				lowerCamelCase_)

       UpperCamelCase										=  torch.tensor([0.0001, 0.0002, 0.0002]).to(lowerCamelCase_)
       self.assertTrue(torch.allclose(outputs.logits[0, :3]					,				lowerCamelCase_					,				atol=1e-4))





"""simple docstring"""







from math import pi





def     __snake_case   (							_lowercase						):


    """simple docstring"""







    return degree / (180 / pi)


if __name__ == "__main__":
  from doctest import testmod

  testmod()