
"""simple docstring"""







import inspect
import unittest

from transformers import ResNetConfig
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 torch import nn

  from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
  from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor






class      __a      :







       def __init__(       self				,lowerCamelCase_				,lowerCamelCase_=3				,lowerCamelCase_=32				,lowerCamelCase_=3				,lowerCamelCase_=10				,lowerCamelCase_=[10, 20, 30, 40]				,lowerCamelCase_=[1, 1, 2, 1]				,lowerCamelCase_=True				,lowerCamelCase_=True				,lowerCamelCase_="relu"				,lowerCamelCase_=3				,lowerCamelCase_=None				,lowerCamelCase_=["stage2", "stage3", "stage4"]				,lowerCamelCase_=[2, 3, 4]				,):



          '''simple docstring'''

          _UpperCAmelCase 						=       parent
          _UpperCAmelCase 						=       batch_size
          _UpperCAmelCase 						=       image_size
          _UpperCAmelCase 						=       num_channels
          _UpperCAmelCase 						=       embeddings_size
          _UpperCAmelCase 						=       hidden_sizes
          _UpperCAmelCase 						=       depths
          _UpperCAmelCase 						=       is_training
          _UpperCAmelCase 						=       use_labels
          _UpperCAmelCase 						=       hidden_act
          _UpperCAmelCase 						=       num_labels
          _UpperCAmelCase 						=       scope
          _UpperCAmelCase 						=       len(lowerCamelCase_		)
          _UpperCAmelCase 						=       out_features
          _UpperCAmelCase 						=       out_indices







       def  _snake_case      (       self		):



          '''simple docstring'''

          _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  _snake_case      (       self		):



          '''simple docstring'''

          return ResNetConfig(
              num_channels=self.num_channels				,embeddings_size=self.embeddings_size				,hidden_sizes=self.hidden_sizes				,depths=self.depths				,hidden_act=self.hidden_act				,num_labels=self.num_labels				,out_features=self.out_features				,out_indices=self.out_indices				,)







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       ResNetModel(config=lowerCamelCase_		)
          model.to(lowerCamelCase_		)
          model.eval()
          _UpperCAmelCase 						=       model(lowerCamelCase_		)
          # 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 // 32, self.image_size // 32)				,)







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       self.num_labels
          _UpperCAmelCase 						=       ResNetForImageClassification(lowerCamelCase_		)
          model.to(lowerCamelCase_		)
          model.eval()
          _UpperCAmelCase 						=       model(lowerCamelCase_				,labels=lowerCamelCase_		)
          self.parent.assertEqual(result.logits.shape				,(self.batch_size, self.num_labels)		)







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       ResNetBackbone(config=lowerCamelCase_		)
          model.to(lowerCamelCase_		)
          model.eval()
          _UpperCAmelCase 						=       model(lowerCamelCase_		)

          # verify feature maps
          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
          _UpperCAmelCase 						=       None
          _UpperCAmelCase 						=       ResNetBackbone(config=lowerCamelCase_		)
          model.to(lowerCamelCase_		)
          model.eval()
          _UpperCAmelCase 						=       model(lowerCamelCase_		)

          # 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  _snake_case      (       self		):



          '''simple docstring'''

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






@require_torch
class      __a      (       lowerCAmelCase_					,						lowerCAmelCase_					,						unittest.TestCase      ):
       _lowerCamelCase				:				Optional[Any]   			=      (
           (
               ResNetModel,
               ResNetForImageClassification,
               ResNetBackbone,
           )
           if is_torch_available()
           else ()
       )
       _lowerCamelCase				:				Union[str, Any]   			=      (
           {'feature-extraction': ResNetModel, 'image-classification': ResNetForImageClassification}
           if is_torch_available()
           else {}
       )

       _lowerCamelCase				:				Dict   			=      True
       _lowerCamelCase				:				Tuple   			=      False
       _lowerCamelCase				:				Dict   			=      False
       _lowerCamelCase				:				Any   			=      False
       _lowerCamelCase				:				Optional[int]   			=      False







       def  _snake_case      (       self		):



          '''simple docstring'''

          _UpperCAmelCase 						=       ResNetModelTester(self		)
          _UpperCAmelCase 						=       ConfigTester(self				,config_class=lowerCamelCase_				,has_text_modality=lowerCamelCase_		)







       def  _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  _snake_case      (       self		):



          '''simple docstring'''

          return







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



          '''simple docstring'''

          pass







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



          '''simple docstring'''

          pass







       def  _snake_case      (       self		):



          '''simple docstring'''

          _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  _snake_case      (       self		):



          '''simple docstring'''

          _UpperCAmelCase 						=       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase_		)







       def  _snake_case      (       self		):



          '''simple docstring'''

          _UpperCAmelCase 						=       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*lowerCamelCase_		)







       def  _snake_case      (       self		):



          '''simple docstring'''

          _UpperCAmelCase						,				_UpperCAmelCase 						=       self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             _UpperCAmelCase 						=       model_class(config=lowerCamelCase_		)
             for name, module in model.named_modules():
                if isinstance(lowerCamelCase_				,(nn.BatchNormad, nn.GroupNorm)		):
                   self.assertTrue(
                       torch.all(module.weight == 1		)				,msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''				,)
                   self.assertTrue(
                       torch.all(module.bias == 0		)				,msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''				,)







       def  _snake_case      (       self		):



          '''simple docstring'''

          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 						=       self.model_tester.num_stages
             self.assertEqual(len(lowerCamelCase_		)				,expected_num_stages + 1		)

             # ResNet'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]				,)

          _UpperCAmelCase						,				_UpperCAmelCase 						=       self.model_tester.prepare_config_and_inputs_for_common()
          _UpperCAmelCase 						=       ["basic", "bottleneck"]
          for model_class in self.all_model_classes:
             for layer_type in layers_type:
                _UpperCAmelCase 						=       layer_type
                _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_		)







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



          '''simple docstring'''

          pass







       def  _snake_case      (       self		):



          '''simple docstring'''

          _UpperCAmelCase 						=       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_		)







       @slow
       def  _snake_case      (       self		):



          '''simple docstring'''

          for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _UpperCAmelCase 						=       ResNetModel.from_pretrained(lowerCamelCase_		)
             self.assertIsNotNone(lowerCamelCase_		)



def       __UpperCAmelCase		(		)  ->							str:
   _UpperCAmelCase 						=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
   return image






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







       @cached_property
       def  _snake_case      (       self		):



          '''simple docstring'''

          return (
              AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
              if is_vision_available()
              else None
          )







       @slow
       def  _snake_case      (       self		):



          '''simple docstring'''

          _UpperCAmelCase 						=       ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).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, 1000)		)
          self.assertEqual(outputs.logits.shape				,lowerCamelCase_		)

          _UpperCAmelCase 						=       torch.tensor([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]		).to(lowerCamelCase_		)

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






@require_torch
class      __a      (       lowerCAmelCase_					,						unittest.TestCase      ):
       _lowerCamelCase				:				str   			=      (ResNetBackbone,) if is_torch_available() else ()
       _lowerCamelCase				:				List[Any]   			=      False
       _lowerCamelCase				:				Any   			=      ResNetConfig







       def  _snake_case      (       self		):



          '''simple docstring'''

          _UpperCAmelCase 						=       ResNetModelTester(self		)



"""simple docstring"""







def       __UpperCAmelCase		(		a_      :		list[int]					)  ->							int:

   # validate:
   if not isinstance(a_     ,	a_					) or not all(
       isinstance(a_     ,	a_					) and item >= 0 for item in citations					):
      raise ValueError("The citations should be a list of non negative integers."					)

   citations.sort()
   _UpperCAmelCase 						=       len(a_					)

   for i in range(a_					):
      if citations[len_citations - 1 - i] <= i:
         return i

   return len_citations


if __name__ == "__main__":
  import doctest

  doctest.testmod()

