




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCamelCase     =				{'''tokenization_bert_japanese''': ['''BertJapaneseTokenizer''', '''CharacterTokenizer''', '''MecabTokenizer''']}


if TYPE_CHECKING:
     from .tokenization_bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer

else:
     import sys

     UpperCamelCase     =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)












'''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 	lowerCAmelCase_  :







  '''simple docstring'''

  def __init__(   self							:       List[str]				,     SCREAMING_SNAKE_CASE_							:       Tuple				,     SCREAMING_SNAKE_CASE_							:       Tuple=13				,     SCREAMING_SNAKE_CASE_							:       int=32				,     SCREAMING_SNAKE_CASE_							:       int=3				,     SCREAMING_SNAKE_CASE_							:       Dict=[3, 3, 5]				,     SCREAMING_SNAKE_CASE_							:       int=[32, 16, 24]				,     SCREAMING_SNAKE_CASE_							:       List[Any]=[16, 24, 40]				,     SCREAMING_SNAKE_CASE_							:       List[str]=[1, 1, 2]				,     SCREAMING_SNAKE_CASE_							:       Optional[Any]=[1, 1, 2]				,     SCREAMING_SNAKE_CASE_							:       List[Any]=[1, 6, 6]				,     SCREAMING_SNAKE_CASE_							:       Dict=True				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]=True				,     SCREAMING_SNAKE_CASE_							:       str=37				,     SCREAMING_SNAKE_CASE_							:       List[Any]="gelu"				,     SCREAMING_SNAKE_CASE_							:       Any=10				,     ) ->		Tuple:







       '''simple docstring'''



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



  def 				_snake_case    (   self							:       List[str]  ) ->		List[str]:







       '''simple docstring'''



       A:		str        =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

       A:		Optional[int]        =		None
       if self.use_labels:
            A:		List[Any]        =		ids_tensor([self.batch_size]				,     self.num_labels  )

       A:		int        =		self.get_config()
       return config, pixel_values, labels



  def 				_snake_case    (   self							:       List[str]  ) ->		str:







       '''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 				_snake_case    (   self							:       int				,     SCREAMING_SNAKE_CASE_							:       Any				,     SCREAMING_SNAKE_CASE_							:       List[Any]				,     SCREAMING_SNAKE_CASE_							:       List[str]  ) ->		Dict:







       '''simple docstring'''



       A:		int        =		EfficientNetModel(config=SCREAMING_SNAKE_CASE_  )
       model.to(SCREAMING_SNAKE_CASE_  )
       model.eval()
       A:		List[Any]        =		model(SCREAMING_SNAKE_CASE_  )
       # 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 				_snake_case    (   self							:       Union[str, Any]				,     SCREAMING_SNAKE_CASE_							:       str				,     SCREAMING_SNAKE_CASE_							:       List[str]				,     SCREAMING_SNAKE_CASE_							:       List[Any]  ) ->		Tuple:







       '''simple docstring'''



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




  def 				_snake_case    (   self							:       Dict  ) ->		Optional[int]:







       '''simple docstring'''



       A:		Any        =		self.prepare_config_and_inputs()
       A    ,						A    ,						A:		str        =		config_and_inputs
       A:		Optional[int]        =		{'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_torch
class 	lowerCAmelCase_  (					UpperCAmelCase_					,				UpperCAmelCase_					,				unittest.TestCase       ):







  '''simple docstring'''

  UpperCamelCase_ :			Dict        =							(EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
  UpperCamelCase_ :			Dict        =							(
      {"""feature-extraction""": EfficientNetModel, """image-classification""": EfficientNetForImageClassification}
      if is_torch_available()
      else {}
  )

  UpperCamelCase_ :			List[Any]        =							False
  UpperCamelCase_ :			Union[str, Any]        =							False
  UpperCamelCase_ :			Union[str, Any]        =							False
  UpperCamelCase_ :			List[Any]        =							False
  UpperCamelCase_ :			str        =							False



  def 				_snake_case    (   self							:       Any  ) ->		str:







       '''simple docstring'''



       A:		List[Any]        =		EfficientNetModelTester(self  )
       A:		List[Any]        =		ConfigTester(
           self				,     config_class=SCREAMING_SNAKE_CASE_				,     has_text_modality=SCREAMING_SNAKE_CASE_				,     hidden_size=37  )



  def 				_snake_case    (   self							:       Tuple  ) ->		int:







       '''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 				_snake_case    (   self							:       List[Any]  ) ->		str:







       '''simple docstring'''



       return



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







       '''simple docstring'''



       pass



  @unittest.skip(reason='''EfficientNet does not support input and output embeddings'''  )
  def 				_snake_case    (   self							:       List[str]  ) ->		Union[str, Any]:







       '''simple docstring'''



       pass



  @unittest.skip(reason='''EfficientNet does not use feedforward chunking'''  )
  def 				_snake_case    (   self							:       Union[str, Any]  ) ->		int:







       '''simple docstring'''



       pass



  def 				_snake_case    (   self							:       str  ) ->		int:







       '''simple docstring'''



       A    ,						A:		List[Any]        =		self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            A:		Optional[int]        =		model_class(SCREAMING_SNAKE_CASE_  )
            A:		Dict        =		inspect.signature(model.forward  )
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            A:		str        =		[*signature.parameters.keys()]

            A:		Any        =		['''pixel_values''']
            self.assertListEqual(arg_names[:1]				,     SCREAMING_SNAKE_CASE_  )



  def 				_snake_case    (   self							:       List[str]  ) ->		Tuple:







       '''simple docstring'''



       A:		Union[str, Any]        =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_  )



  def 				_snake_case    (   self							:       Union[str, Any]  ) ->		Optional[int]:







       '''simple docstring'''



       def check_hidden_states_output(SCREAMING_SNAKE_CASE_							:       int				,     SCREAMING_SNAKE_CASE_							:       Any				,     SCREAMING_SNAKE_CASE_							:       int  ):
            A:		int        =		model_class(SCREAMING_SNAKE_CASE_  )
            model.to(SCREAMING_SNAKE_CASE_  )
            model.eval()

            with torch.no_grad():
                 A:		List[str]        =		model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )  )

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

            # 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]				,     )

       A    ,						A:		List[Any]        =		self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            A:		Union[str, Any]        =		True
            check_hidden_states_output(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )

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

            check_hidden_states_output(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )



  def 				_snake_case    (   self							:       str  ) ->		List[str]:







       '''simple docstring'''



       A:		Dict        =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_  )



  @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
  def 				_snake_case    (   self							:       Optional[int]  ) ->		Union[str, Any]:







       '''simple docstring'''



       pass



  @slow
  def 				_snake_case    (   self							:       Any  ) ->		int:







       '''simple docstring'''



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




  @is_pipeline_test
  @require_vision
  @slow
  def 				_snake_case    (   self							:       int  ) ->		str:







       '''simple docstring'''



       super().test_pipeline_image_classification()






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






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







  '''simple docstring'''

  @cached_property
  def 				_snake_case    (   self							:       Dict  ) ->		List[str]:







       '''simple docstring'''



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




  @slow
  def 				_snake_case    (   self							:       Optional[Any]  ) ->		Optional[int]:







       '''simple docstring'''



       A:		str        =		EfficientNetForImageClassification.from_pretrained('''google/efficientnet-b7'''  ).to(SCREAMING_SNAKE_CASE_  )

       A:		Optional[int]        =		self.default_image_processor
       A:		Tuple        =		prepare_img()
       A:		List[Any]        =		image_processor(images=SCREAMING_SNAKE_CASE_				,     return_tensors='''pt'''  ).to(SCREAMING_SNAKE_CASE_  )

       # forward pass
       with torch.no_grad():
            A:		List[str]        =		model(**SCREAMING_SNAKE_CASE_  )

       # verify the logits
       A:		List[str]        =		torch.Size((1, 10_00)  )
       self.assertEqual(outputs.logits.shape				,     SCREAMING_SNAKE_CASE_  )

       A:		Union[str, Any]        =		torch.tensor([0.0001, 0.0002, 0.0002]  ).to(SCREAMING_SNAKE_CASE_  )
       self.assertTrue(torch.allclose(outputs.logits[0, :3]				,     SCREAMING_SNAKE_CASE_				,     atol=1E-4  )  )






