



"""simple docstring"""






import inspect
import unittest

from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
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 ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
   from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor






class        __snake_case       :




  def __init__( self     :						Optional[int]						,					__lowerCAmelCase     :						List[str]						,					__lowerCAmelCase     :						Any=1_3						,					__lowerCAmelCase     :						Optional[int]=3_2						,					__lowerCAmelCase     :						Any=3						,					__lowerCAmelCase     :						Tuple=4						,					__lowerCAmelCase     :						str=[1_0, 2_0, 3_0, 4_0]						,					__lowerCAmelCase     :						str=[2, 2, 3, 2]						,					__lowerCAmelCase     :						Dict=True						,					__lowerCAmelCase     :						List[str]=True						,					__lowerCAmelCase     :						str=3_7						,					__lowerCAmelCase     :						Union[str, Any]="gelu"						,					__lowerCAmelCase     :						Dict=1_0						,					__lowerCAmelCase     :						Union[str, Any]=0.02						,					__lowerCAmelCase     :						int=["stage2", "stage3", "stage4"]						,					__lowerCAmelCase     :						List[str]=[2, 3, 4]						,					__lowerCAmelCase     :						Any=None						,					):






         """simple docstring"""






         _lowerCamelCase						:				Dict    			= parent
         _lowerCamelCase						:				str    			= batch_size
         _lowerCamelCase						:				Tuple    			= image_size
         _lowerCamelCase						:				Union[str, Any]    			= num_channels
         _lowerCamelCase						:				Dict    			= num_stages
         _lowerCamelCase						:				Dict    			= hidden_sizes
         _lowerCamelCase						:				Tuple    			= depths
         _lowerCamelCase						:				Any    			= is_training
         _lowerCamelCase						:				Optional[Any]    			= use_labels
         _lowerCamelCase						:				List[str]    			= intermediate_size
         _lowerCamelCase						:				int    			= hidden_act
         _lowerCamelCase						:				str    			= num_labels
         _lowerCamelCase						:				Tuple    			= initializer_range
         _lowerCamelCase						:				List[str]    			= out_features
         _lowerCamelCase						:				Union[str, Any]    			= out_indices
         _lowerCamelCase						:				Dict    			= scope




  def 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]		):






         """simple docstring"""






         _lowerCamelCase						:				Any    			= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

         _lowerCamelCase						:				Optional[Any]    			= self.get_config()
         return config, pixel_values, labels




  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]		):






         """simple docstring"""






         return ConvNextConfig(
             num_channels=self.num_channels						,					hidden_sizes=self.hidden_sizes						,					depths=self.depths						,					num_stages=self.num_stages						,					hidden_act=self.hidden_act						,					is_decoder=UpperCamelCase__						,					initializer_range=self.initializer_range						,					out_features=self.out_features						,					out_indices=self.out_indices						,					num_labels=self.num_labels						,					)




  def 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]						,					__lowerCAmelCase     :						List[Any]						,					__lowerCAmelCase     :						Any						,					__lowerCAmelCase     :						str		):






         """simple docstring"""






         _lowerCamelCase						:				Any    			= ConvNextModel(config=UpperCamelCase__		)
         model.to(UpperCamelCase__		)
         model.eval()
         _lowerCamelCase						:				List[Any]    			= model(UpperCamelCase__		)
         # expected last hidden states: B, C, H // 32, W // 32
         self.parent.assertEqual(
             result.last_hidden_state.shape						,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)						,					)




  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]						,					__lowerCAmelCase     :						Union[str, Any]						,					__lowerCAmelCase     :						Optional[Any]						,					__lowerCAmelCase     :						int		):






         """simple docstring"""






         _lowerCamelCase						:				Union[str, Any]    			= ConvNextForImageClassification(UpperCamelCase__		)
         model.to(UpperCamelCase__		)
         model.eval()
         _lowerCamelCase						:				Union[str, Any]    			= model(UpperCamelCase__						,					labels=UpperCamelCase__		)
         self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.num_labels)		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Tuple						,					__lowerCAmelCase     :						Optional[int]						,					__lowerCAmelCase     :						Tuple						,					__lowerCAmelCase     :						str		):






         """simple docstring"""






         _lowerCamelCase						:				Any    			= ConvNextBackbone(config=UpperCamelCase__		)
         model.to(UpperCamelCase__		)
         model.eval()
         _lowerCamelCase						:				List[Any]    			= model(UpperCamelCase__		)

         # verify hidden states
         self.parent.assertEqual(len(result.feature_maps		)						,					len(config.out_features		)		)
         self.parent.assertListEqual(list(result.feature_maps[0].shape		)						,					[self.batch_size, self.hidden_sizes[1], 4, 4]		)

         # verify channels
         self.parent.assertEqual(len(model.channels		)						,					len(config.out_features		)		)
         self.parent.assertListEqual(model.channels						,					config.hidden_sizes[1:]		)

         # verify backbone works with out_features=None
         _lowerCamelCase						:				List[Any]    			= None
         _lowerCamelCase						:				Optional[int]    			= ConvNextBackbone(config=UpperCamelCase__		)
         model.to(UpperCamelCase__		)
         model.eval()
         _lowerCamelCase						:				List[Any]    			= model(UpperCamelCase__		)

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps		)						,					1		)
         self.parent.assertListEqual(list(result.feature_maps[0].shape		)						,					[self.batch_size, self.hidden_sizes[-1], 1, 1]		)

         # verify channels
         self.parent.assertEqual(len(model.channels		)						,					1		)
         self.parent.assertListEqual(model.channels						,					[config.hidden_sizes[-1]]		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Any		):






         """simple docstring"""






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






@require_torch
class        __snake_case       (	_a  ,     _a  ,     unittest.TestCase):
  snake_case__				:   List[Any]  			=						(
      (
          ConvNextModel,
          ConvNextForImageClassification,
          ConvNextBackbone,
      )
      if is_torch_available()
      else ()
  )
  snake_case__				:   List[str]  			=						(
      {"feature-extraction": ConvNextModel, "image-classification": ConvNextForImageClassification}
      if is_torch_available()
      else {}
  )

  snake_case__				:   Optional[Any]  			=						True
  snake_case__				:   Union[str, Any]  			=						False
  snake_case__				:   Tuple  			=						False
  snake_case__				:   str  			=						False
  snake_case__				:   Any  			=						False




  def 						SCREAMING_SNAKE_CASE				( self     :						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:				List[str]    			= ConvNextModelTester(self		)
         _lowerCamelCase						:				str    			= ConfigTester(self						,					config_class=UpperCamelCase__						,					has_text_modality=UpperCamelCase__						,					hidden_size=3_7		)




  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]		):






         """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     :						Optional[int]		):






         """simple docstring"""






         return




  @unittest.skip(reason='''ConvNext does not use inputs_embeds'''		)
  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]		):






         """simple docstring"""






         pass




  @unittest.skip(reason='''ConvNext does not support input and output embeddings'''		)
  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]		):






         """simple docstring"""






         pass




  @unittest.skip(reason='''ConvNext does not use feedforward chunking'''		)
  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[int]		):






         """simple docstring"""






         pass




  def 						SCREAMING_SNAKE_CASE				( self     :						Any		):






         """simple docstring"""






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

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

                _lowerCamelCase						:				List[Any]    			= ['''pixel_values''']
                self.assertListEqual(arg_names[:1]						,					UpperCamelCase__		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]		):






         """simple docstring"""






         _lowerCamelCase						:				str    			= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*UpperCamelCase__		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:				List[Any]    			= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*UpperCamelCase__		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[Any]		):






         """simple docstring"""






         def check_hidden_states_output(__lowerCAmelCase     :						Dict						,					__lowerCAmelCase     :						Union[str, Any]						,					__lowerCAmelCase     :						Tuple		):
                _lowerCamelCase						:				Dict    			= model_class(UpperCamelCase__		)
                model.to(UpperCamelCase__		)
                model.eval()

                with torch.no_grad():
                       _lowerCamelCase						:				Any    			= model(**self._prepare_for_class(UpperCamelCase__						,					UpperCamelCase__		)		)

                _lowerCamelCase						:				int    			= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                _lowerCamelCase						:				Any    			= self.model_tester.num_stages
                self.assertEqual(len(UpperCamelCase__		)						,					expected_num_stages + 1		)

                # ConvNext'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 // 4, self.model_tester.image_size // 4]						,					)

         _lowerCamelCase			,    _lowerCamelCase						:				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(UpperCamelCase__						,					UpperCamelCase__						,					UpperCamelCase__		)

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

                check_hidden_states_output(UpperCamelCase__						,					UpperCamelCase__						,					UpperCamelCase__		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Dict		):






         """simple docstring"""






         _lowerCamelCase						:				Optional[int]    			= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__		)




  @slow
  def 						SCREAMING_SNAKE_CASE				( self     :						Dict		):






         """simple docstring"""






         for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _lowerCamelCase						:				List[str]    			= ConvNextModel.from_pretrained(UpperCamelCase__		)
                self.assertIsNotNone(UpperCamelCase__		)






def        snake_case_  (      ):


       '''simple docstring'''





       _lowerCamelCase						:				List[str]    			= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
       return image






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




  @cached_property
  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[Any]		):






         """simple docstring"""






         return AutoImageProcessor.from_pretrained('''facebook/convnext-tiny-224'''		) if is_vision_available() else None




  @slow
  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]		):






         """simple docstring"""






         _lowerCamelCase						:				Tuple    			= ConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224'''		).to(UpperCamelCase__		)

         _lowerCamelCase						:				Optional[int]    			= self.default_image_processor
         _lowerCamelCase						:				str    			= prepare_img()
         _lowerCamelCase						:				Union[str, Any]    			= image_processor(images=UpperCamelCase__						,					return_tensors='''pt'''		).to(UpperCamelCase__		)

         # forward pass
         with torch.no_grad():
                _lowerCamelCase						:				str    			= model(**UpperCamelCase__		)

         # verify the logits
         _lowerCamelCase						:				int    			= torch.Size((1, 1_0_0_0)		)
         self.assertEqual(outputs.logits.shape						,					UpperCamelCase__		)

         _lowerCamelCase						:				Optional[Any]    			= torch.tensor([-0.02_60, -0.47_39, 0.19_11]		).to(UpperCamelCase__		)

         self.assertTrue(torch.allclose(outputs.logits[0, :3]						,					UpperCamelCase__						,					atol=1E-4		)		)






@require_torch
class        __snake_case       (	unittest.TestCase  ,     _a):
  snake_case__				:   List[Any]  			=						(ConvNextBackbone,) if is_torch_available() else ()
  snake_case__				:   Optional[Any]  			=						ConvNextConfig

  snake_case__				:   Tuple  			=						False




  def 						SCREAMING_SNAKE_CASE				( self     :						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:				Tuple    			= ConvNextModelTester(self		)







'''simple docstring'''


from PIL import Image







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Image:






   """simple docstring"""

   def brightness(A__    ) -> float:
      return 128 + level + (c - 128)

   if not -255.0 <= level <= 255.0:
      raise ValueError('level must be between -255.0 (black) and 255.0 (white)'    )
   return img.point(A__    )


if __name__ == "__main__":
   # Load image
   with Image.open("image_data/lena.jpg") as img:
      # Change brightness to 100
      _lowerCamelCase	:       List[str]      	=	change_brightness(img, 100)
      brigt_img.save("image_data/lena_brightness.png", format="png")

