

"""simple docstring"""

import math
import os

import cva
import numpy as np

_UpperCamelCase		    =						2_5_5.0



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__      ) ->	Optional[Any]:
 lowerCAmelCase__      :							List[str]    =							np.mean((original - contrast) ** 2      )
 if mse == 0:
  return 1_0_0

 return 2_0 * math.logaa(PIXEL_MAX / math.sqrt(_A      )      )



def 			SCREAMING_SNAKE_CASE       (   ) ->	List[str]:
 lowerCAmelCase__      :							Optional[int]    =							os.path.dirname(os.path.realpath(_A      )      )
 # Loading images (original image and compressed image)
 lowerCAmelCase__      :							Optional[int]    =							cva.imread(os.path.join(_A				,				"image_data/original_image.png"      )      )
 lowerCAmelCase__      :							Tuple    =							cva.imread(os.path.join(_A				,				"image_data/compressed_image.png"      )				,				1      )

 lowerCAmelCase__      :							Tuple    =							cva.imread(os.path.join(_A				,				"image_data/PSNR-example-base.png"      )      )
 lowerCAmelCase__      :							Any    =							cva.imread(
     os.path.join(_A				,				"image_data/PSNR-example-comp-10.jpg"      )				,				1      )

 # Value expected: 29.73dB
 print("-- First Test --"      )
 print(F"""PSNR value is {peak_signal_to_noise_ratio(_A				,				_A      )} dB"""      )

 # # Value expected: 31.53dB (Wikipedia Example)
 print("\n-- Second Test --"      )
 print(F"""PSNR value is {peak_signal_to_noise_ratio(_A				,				_A      )} dB"""      )


if __name__ == "__main__":
    main()









"""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  __a				:





       """simple docstring"""


       def __init__(      self ,				snake_case ,				snake_case=13 ,				snake_case=32 ,				snake_case=3 ,				snake_case=[3, 3, 5] ,				snake_case=[32, 16, 24] ,				snake_case=[16, 24, 40] ,				snake_case=[1, 1, 2] ,				snake_case=[1, 1, 2] ,				snake_case=[1, 6, 6] ,				snake_case=True ,				snake_case=True ,				snake_case=37 ,				snake_case="gelu" ,				snake_case=10 ,				):





        """simple docstring"""

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






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Dict    =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

        lowerCAmelCase__      :							List[str]    =							None
        if self.use_labels:
         lowerCAmelCase__      :							int    =							ids_tensor([self.batch_size] ,				self.num_labels      )

        lowerCAmelCase__      :							List[str]    =							self.get_config()
        return config, pixel_values, labels






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """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  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							EfficientNetModel(config=snake_case      )
        model.to(snake_case      )
        model.eval()
        lowerCAmelCase__      :							Tuple    =							model(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  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							int    =							EfficientNetForImageClassification(snake_case      )
        model.to(snake_case      )
        model.eval()
        lowerCAmelCase__      :							str    =							model(snake_case ,				labels=snake_case      )
        self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							self.prepare_config_and_inputs()
        lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							Dict    =							config_and_inputs
        lowerCAmelCase__      :							Dict    =							{"pixel_values": pixel_values}
        return config, inputs_dict






@require_torch
class  __a				(	__magic_name__					,  __magic_name__					,  unittest.TestCase					):





       """simple docstring"""


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

       __UpperCamelCase				:			str          =	False
       __UpperCamelCase				:			List[Any]          =	False
       __UpperCamelCase				:			Dict          =	False
       __UpperCamelCase				:			Optional[int]          =	False
       __UpperCamelCase				:			List[Any]          =	False






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							EfficientNetModelTester(self      )
        lowerCAmelCase__      :							Tuple    =							ConfigTester(
            self ,				config_class=snake_case ,				has_text_modality=snake_case ,				hidden_size=37      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """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  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        return






       @unittest.skip(reason="EfficientNet does not use inputs_embeds"      )
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






       @unittest.skip(reason="EfficientNet does not support input and output embeddings"      )
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






       @unittest.skip(reason="EfficientNet does not use feedforward chunking"      )
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

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

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

         lowerCAmelCase__      :							List[str]    =							["pixel_values"]
         self.assertListEqual(arg_names[:1] ,				snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[Any]    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        def check_hidden_states_output(snake_case ,				snake_case ,				snake_case      ):
         lowerCAmelCase__      :							Tuple    =							model_class(snake_case      )
         model.to(snake_case      )
         model.eval()

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

         lowerCAmelCase__      :							Tuple    =							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
         lowerCAmelCase__      :							Any    =							sum(config.num_block_repeats      ) * 4
         self.assertEqual(len(snake_case      ) ,				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] ,				)

        lowerCAmelCase__ ,	lowerCAmelCase__      :							Optional[int]    =							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowerCAmelCase__      :							str    =							True
         check_hidden_states_output(snake_case ,				snake_case ,				snake_case      )

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

         check_hidden_states_output(snake_case ,				snake_case ,				snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[Any]    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*snake_case      )






       @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."      )
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






       @slow
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

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






       @is_pipeline_test
       @require_vision
       @slow
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        super().test_pipeline_image_classification()




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






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





       """simple docstring"""


       @cached_property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

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






       @slow
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[Any]    =							EfficientNetForImageClassification.from_pretrained("google/efficientnet-b7"      ).to(snake_case      )

        lowerCAmelCase__      :							Union[str, Any]    =							self.default_image_processor
        lowerCAmelCase__      :							Tuple    =							prepare_img()
        lowerCAmelCase__      :							Dict    =							image_processor(images=snake_case ,				return_tensors="pt"      ).to(snake_case      )

        # forward pass
        with torch.no_grad():
         lowerCAmelCase__      :							str    =							model(**snake_case      )

        # verify the logits
        lowerCAmelCase__      :							Any    =							torch.Size((1, 1_000)      )
        self.assertEqual(outputs.logits.shape ,				snake_case      )

        lowerCAmelCase__      :							Union[str, Any]    =							torch.tensor([0.0_001, 0.0_002, 0.0_002]      ).to(snake_case      )
        self.assertTrue(torch.allclose(outputs.logits[0, :3] ,				snake_case ,				atol=1e-4      )      )






