code: 
  
  
  
  
  """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 							lowerCamelCase		:
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=3    ,      SCREAMING_SNAKE_CASE_=32    ,      SCREAMING_SNAKE_CASE_=3    ,      SCREAMING_SNAKE_CASE_=10    ,      SCREAMING_SNAKE_CASE_=[10, 20, 30, 40]    ,      SCREAMING_SNAKE_CASE_=[1, 1, 2, 1]    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_="relu"    ,      SCREAMING_SNAKE_CASE_=3    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=["stage2", "stage3", "stage4"]    ,      SCREAMING_SNAKE_CASE_=[2, 3, 4]    ,      ):
         UpperCamelCase				:   Tuple          =							parent
         UpperCamelCase				:   Any          =							batch_size
         UpperCamelCase				:   Optional[int]          =							image_size
         UpperCamelCase				:   Dict          =							num_channels
         UpperCamelCase				:   Tuple          =							embeddings_size
         UpperCamelCase				:   Dict          =							hidden_sizes
         UpperCamelCase				:   int          =							depths
         UpperCamelCase				:   int          =							is_training
         UpperCamelCase				:   Any          =							use_labels
         UpperCamelCase				:   Optional[int]          =							hidden_act
         UpperCamelCase				:   List[str]          =							num_labels
         UpperCamelCase				:   List[str]          =							scope
         UpperCamelCase				:   Optional[Any]          =							len(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   List[Any]          =							out_features
         UpperCamelCase				:   List[Any]          =							out_indices
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   Dict          =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
  
         UpperCamelCase				:   Optional[Any]          =							None
         if self.use_labels:
             UpperCamelCase				:   Any          =							ids_tensor([self.batch_size]    ,      self.num_labels )
  
         UpperCamelCase				:   Optional[Any]          =							self.get_config()
  
         return config, pixel_values, labels
  
  
  
  
  
  
  
     def       a_							(							self ):
         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       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   List[Any]          =							ResNetModel(config=SCREAMING_SNAKE_CASE_ )
         model.to(SCREAMING_SNAKE_CASE_ )
         model.eval()
         UpperCamelCase				:   List[Any]          =							model(SCREAMING_SNAKE_CASE_ )
         # 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       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Union[str, Any]          =							self.num_labels
         UpperCamelCase				:   Union[str, Any]          =							ResNetForImageClassification(SCREAMING_SNAKE_CASE_ )
         model.to(SCREAMING_SNAKE_CASE_ )
         model.eval()
         UpperCamelCase				:   Union[str, Any]          =							model(SCREAMING_SNAKE_CASE_    ,      labels=SCREAMING_SNAKE_CASE_ )
         self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.num_labels) )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   List[Any]          =							ResNetBackbone(config=SCREAMING_SNAKE_CASE_ )
         model.to(SCREAMING_SNAKE_CASE_ )
         model.eval()
         UpperCamelCase				:   Any          =							model(SCREAMING_SNAKE_CASE_ )
  
         # 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				:   Any          =							None
         UpperCamelCase				:   List[Any]          =							ResNetBackbone(config=SCREAMING_SNAKE_CASE_ )
         model.to(SCREAMING_SNAKE_CASE_ )
         model.eval()
         UpperCamelCase				:   Any          =							model(SCREAMING_SNAKE_CASE_ )
  
         # 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       a_							(							self ):
         UpperCamelCase				:   Dict          =							self.prepare_config_and_inputs()
         UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase				:   Any          =							config_and_inputs
         UpperCamelCase				:   List[str]          =							{"""pixel_values""": pixel_values}
         return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class 							lowerCamelCase		( _UpperCAmelCase	,  _UpperCAmelCase	,  unittest.TestCase				):
     lowercase       :  Optional[Any]      =				(
         (
             ResNetModel,
             ResNetForImageClassification,
             ResNetBackbone,
         )
         if is_torch_available()
         else ()
     )
     lowercase       :  Tuple      =				(
         {'feature-extraction': ResNetModel, 'image-classification': ResNetForImageClassification}
         if is_torch_available()
         else {}
     )
  
     lowercase       :  Any      =				True
     lowercase       :  Optional[int]      =				False
     lowercase       :  List[str]      =				False
     lowercase       :  int      =				False
     lowercase       :  List[Any]      =				False
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   Optional[Any]          =							ResNetModelTester(self )
         UpperCamelCase				:   List[Any]          =							ConfigTester(self    ,      config_class=SCREAMING_SNAKE_CASE_    ,      has_text_modality=SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self ):
         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       a_							(							self ):
         return
  
  
  
  
  
  
  
     @unittest.skip(reason="""ResNet does not use inputs_embeds""" )
     def       a_							(							self ):
         pass
  
  
  
  
  
  
  
     @unittest.skip(reason="""ResNet does not support input and output embeddings""" )
     def       a_							(							self ):
         pass
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase       ,							UpperCamelCase				:   Union[str, Any]          =							self.model_tester.prepare_config_and_inputs_for_common()
  
         for model_class in self.all_model_classes:
             UpperCamelCase				:   int          =							model_class(SCREAMING_SNAKE_CASE_ )
             UpperCamelCase				:   Dict          =							inspect.signature(model.forward )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             UpperCamelCase				:   int          =							[*signature.parameters.keys()]
  
             UpperCamelCase				:   Any          =							["""pixel_values"""]
             self.assertListEqual(arg_names[:1]    ,      SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   List[str]          =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   Optional[int]          =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase       ,							UpperCamelCase				:   Dict          =							self.model_tester.prepare_config_and_inputs_for_common()
  
         for model_class in self.all_model_classes:
             UpperCamelCase				:   Any          =							model_class(config=SCREAMING_SNAKE_CASE_ )
             for name, module in model.named_modules():
                 if isinstance(SCREAMING_SNAKE_CASE_    ,      (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       a_							(							self ):
         def check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
             UpperCamelCase				:   int          =							model_class(SCREAMING_SNAKE_CASE_ )
             model.to(SCREAMING_SNAKE_CASE_ )
             model.eval()
  
             with torch.no_grad():
                 UpperCamelCase				:   str          =							model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ) )
  
             UpperCamelCase				:   Any          =							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  
             UpperCamelCase				:   Tuple          =							self.model_tester.num_stages
             self.assertEqual(len(SCREAMING_SNAKE_CASE_ )    ,      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				:   Optional[int]          =							self.model_tester.prepare_config_and_inputs_for_common()
         UpperCamelCase				:   Dict          =							["""basic""", """bottleneck"""]
         for model_class in self.all_model_classes:
             for layer_type in layers_type:
                 UpperCamelCase				:   Tuple          =							layer_type
                 UpperCamelCase				:   Optional[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"]
                 UpperCamelCase				:   Dict          =							True
  
                 check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     @unittest.skip(reason="""ResNet does not use feedforward chunking""" )
     def       a_							(							self ):
         pass
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   Any          =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     @slow
     def       a_							(							self ):
         for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase				:   Dict          =							ResNetModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
  def 					A_			(		):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Optional[int]          =							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
      return image
  @require_torch
  @require_vision
  class 							lowerCamelCase		( unittest.TestCase				):
  
  
  
  
  
  
  
     @cached_property
     def       a_							(							self ):
         return (
             AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
             if is_vision_available()
             else None
         )
  
  
  
  
  
  
  
     @slow
     def       a_							(							self ):
         UpperCamelCase				:   str          =							ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase				:   str          =							self.default_image_processor
         UpperCamelCase				:   str          =							prepare_img()
         UpperCamelCase				:   Union[str, Any]          =							image_processor(images=SCREAMING_SNAKE_CASE_    ,      return_tensors="""pt""" ).to(SCREAMING_SNAKE_CASE_ )
  
         # forward pass
         with torch.no_grad():
             UpperCamelCase				:   List[str]          =							model(**SCREAMING_SNAKE_CASE_ )
  
         # verify the logits
         UpperCamelCase				:   Optional[Any]          =							torch.Size((1, 1000) )
         self.assertEqual(outputs.logits.shape    ,      SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase				:   Any          =							torch.tensor([-11.1069, -9.7877, -8.3777] ).to(SCREAMING_SNAKE_CASE_ )
  
         self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,      SCREAMING_SNAKE_CASE_    ,      atol=1e-4 ) )
  
  
  
  
  
  
  
  @require_torch
  class 							lowerCamelCase		( _UpperCAmelCase	,  unittest.TestCase				):
     lowercase       :  List[Any]      =				(ResNetBackbone,) if is_torch_available() else ()
     lowercase       :  Union[str, Any]      =				False
     lowercase       :  Optional[Any]      =				ResNetConfig
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   Any          =							ResNetModelTester(self )
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import numpy as np
  import PIL
  import torch
  import torchvision.transforms as T
  import torchvision.transforms.functional as TF
  from PIL import Image
  def 					A_			(		snake_case_     :       List[str]						,snake_case_     :       Union[str, Any]=2_5_6    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Optional[int]          =							min(img.size    )
  
      if s < target_image_size:
          raise ValueError(f'min dim for image {s} < {target_image_size}'    )
  
      UpperCamelCase				:   Dict          =							target_image_size / s
      UpperCamelCase				:   Any          =							(round(r * img.size[1]    ), round(r * img.size[0]    ))
      UpperCamelCase				:   Union[str, Any]          =							TF.resize(snake_case_						,snake_case_						,interpolation=PIL.Image.LANCZOS    )
      UpperCamelCase				:   Optional[int]          =							TF.center_crop(snake_case_						,output_size=2 * [target_image_size]    )
      UpperCamelCase				:   Optional[Any]          =							torch.unsqueeze(T.ToTensor()(snake_case_    )						,0    )
      return img
  def 					A_			(		snake_case_     :       List[Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Any          =							2.0 * x - 1.0
      return x
  def 					A_			(		snake_case_     :       Tuple						,snake_case_     :       Optional[Any]=True						,snake_case_     :       Dict="RGB"    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Dict          =							x.detach().cpu()
      if process:
          UpperCamelCase				:   str          =							post_process_tensor(snake_case_    )
      UpperCamelCase				:   List[Any]          =							x.numpy()
      if process:
          UpperCamelCase				:   Optional[int]          =							(2_5_5 * x).astype(np.uinta    )
      UpperCamelCase				:   List[Any]          =							Image.fromarray(snake_case_    )
      if not x.mode == mode:
          UpperCamelCase				:   Any          =							x.convert(snake_case_    )
      return x
  def 					A_			(		snake_case_     :       int    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   int          =							torch.clamp(snake_case_						,-1.0						,1.0    )
      UpperCamelCase				:   Optional[Any]          =							(x + 1.0) / 2.0
      UpperCamelCase				:   Dict          =							x.permute(1						,2						,0    )
      return x
  
  
  def 					A_			(		snake_case_     :       int    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Optional[Any]          =							post_process_tensor(x.squeeze()    )
      return x.permute(2						,0						,1    ).unsqueeze(0    )
  
  
  
  
  