code: 
  '''simple docstring'''
  
  
  
  
  
  import copy
  import inspect
  
  from transformers.testing_utils import require_torch, torch_device
  from transformers.utils.backbone_utils import BackboneType
  
  
  
  @require_torch
  class  lowerCAmelCase__   :
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    _UpperCamelCase						= ()
    _UpperCamelCase						= True
  
    def 		_lowerCamelCase	(self  : str    )   ->       Any:
          snake_case_						       =							self.config_class
  
          # test default config
          snake_case_						       =							config_class()
          self.assertIsNotNone(__UpperCamelCase    )
          snake_case_						       =							['''stem'''] + [f'''stage{idx}''' for idx in range(1	, len(config.depths    ) + 1    )]
          self.assertEqual(config.stage_names	, __UpperCamelCase    )
          self.assertTrue(set(config.out_features    ).issubset(set(config.stage_names    )    )    )
  
          # Test out_features and out_indices are correctly set
          # out_features and out_indices both None
          snake_case_						       =							config_class(out_features=__UpperCamelCase	, out_indices=__UpperCamelCase    )
          self.assertEqual(config.out_features	, [config.stage_names[-1]]    )
          self.assertEqual(config.out_indices	, [len(config.stage_names    ) - 1]    )
  
          # out_features and out_indices both set
          snake_case_						       =							config_class(out_features=["stem", "stage1"]	, out_indices=[0, 1]    )
          self.assertEqual(config.out_features	, ["stem", "stage1"]    )
          self.assertEqual(config.out_indices	, [0, 1]    )
  
          # Only out_features set
          snake_case_						       =							config_class(out_features=["stage1", "stage3"]    )
          self.assertEqual(config.out_features	, ["stage1", "stage3"]    )
          self.assertEqual(config.out_indices	, [1, 3]    )
  
          # Only out_indices set
          snake_case_						       =							config_class(out_indices=[0, 2]    )
          self.assertEqual(config.out_features	, [config.stage_names[0], config.stage_names[2]]    )
          self.assertEqual(config.out_indices	, [0, 2]    )
  
          # Error raised when out_indices do not correspond to out_features
          with self.assertRaises(__UpperCamelCase    ):
                snake_case_						       =							config_class(out_features=["stage1", "stage2"]	, out_indices=[0, 2]    )
  
    def 		_lowerCamelCase	(self  : Dict    )   ->       int:
          snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
                snake_case_						       =							model_class(__UpperCamelCase    )
                snake_case_						       =							inspect.signature(model.forward    )
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                snake_case_						       =							[*signature.parameters.keys()]
                snake_case_						       =							['''pixel_values''']
                self.assertListEqual(arg_names[:1]	, __UpperCamelCase    )
  
    def 		_lowerCamelCase	(self  : Optional[int]    )   ->       Tuple:
          snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
                snake_case_						       =							model_class(__UpperCamelCase    )
                self.assertEqual(len(model.channels    )	, len(config.out_features    )    )
                snake_case_						       =							model.num_features
                snake_case_						       =							[config.stage_names.index(__UpperCamelCase    ) for feat in config.out_features]
                snake_case_						       =							[num_features[idx] for idx in out_indices]
                self.assertListEqual(model.channels	, __UpperCamelCase    )
  
                snake_case_						       =							copy.deepcopy(__UpperCamelCase    )
                snake_case_						       =							None
                snake_case_						       =							model_class(__UpperCamelCase    )
                self.assertEqual(len(model.channels    )	, 1    )
                self.assertListEqual(model.channels	, [num_features[-1]]    )
  
                snake_case_						       =							copy.deepcopy(__UpperCamelCase    )
                snake_case_						       =							None
                snake_case_						       =							model_class(__UpperCamelCase    )
                self.assertEqual(len(model.channels    )	, 1    )
                self.assertListEqual(model.channels	, [num_features[-1]]    )
  
    def 		_lowerCamelCase	(self  : List[Any]    )   ->       str:
          snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
                snake_case_						       =							model_class(__UpperCamelCase    )
                model.to(__UpperCamelCase    )
                model.eval()
                snake_case_						       =							model(**__UpperCamelCase    )
  
                self.assertEqual(len(result.feature_maps    )	, len(config.out_features    )    )
                self.assertEqual(len(model.channels    )	, len(config.out_features    )    )
                self.assertEqual(len(result.feature_maps    )	, len(config.out_indices    )    )
                self.assertEqual(len(model.channels    )	, len(config.out_indices    )    )
  
                # Check output of last stage is taken if out_features=None, out_indices=None
                snake_case_						       =							copy.deepcopy(__UpperCamelCase    )
                snake_case_						       =							None
                snake_case_						       =							model_class(__UpperCamelCase    )
                model.to(__UpperCamelCase    )
                model.eval()
                snake_case_						       =							model(**__UpperCamelCase    )
  
                self.assertEqual(len(result.feature_maps    )	, 1    )
                self.assertEqual(len(model.channels    )	, 1    )
  
                snake_case_						       =							copy.deepcopy(__UpperCamelCase    )
                snake_case_						       =							None
                snake_case_						       =							model_class(__UpperCamelCase    )
                model.to(__UpperCamelCase    )
                model.eval()
                snake_case_						       =							model(**__UpperCamelCase    )
  
                self.assertEqual(len(result.feature_maps    )	, 1    )
                self.assertEqual(len(model.channels    )	, 1    )
  
                # Check backbone can be initialized with fresh weights
                snake_case_						       =							copy.deepcopy(__UpperCamelCase    )
                snake_case_						       =							False
                snake_case_						       =							model_class(__UpperCamelCase    )
                model.to(__UpperCamelCase    )
                model.eval()
                snake_case_						       =							model(**__UpperCamelCase    )
  
    def 		_lowerCamelCase	(self  : Union[str, Any]    )   ->       List[Any]:
          snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()
  
          for backbone_class in self.all_model_classes:
                snake_case_						       =							backbone_class(__UpperCamelCase    )
  
                self.assertTrue(hasattr(__UpperCamelCase	, "backbone_type"    )    )
                self.assertTrue(hasattr(__UpperCamelCase	, "stage_names"    )    )
                self.assertTrue(hasattr(__UpperCamelCase	, "num_features"    )    )
                self.assertTrue(hasattr(__UpperCamelCase	, "out_indices"    )    )
                self.assertTrue(hasattr(__UpperCamelCase	, "out_features"    )    )
                self.assertTrue(hasattr(__UpperCamelCase	, "out_feature_channels"    )    )
                self.assertTrue(hasattr(__UpperCamelCase	, "channels"    )    )
  
                self.assertIsInstance(backbone.backbone_type	, __UpperCamelCase    )
                # Verify num_features has been initialized in the backbone init
                self.assertIsNotNone(backbone.num_features    )
                self.assertTrue(len(backbone.channels    ) == len(backbone.out_indices    )    )
                self.assertTrue(len(backbone.stage_names    ) == len(backbone.num_features    )    )
                self.assertTrue(len(backbone.channels    ) <= len(backbone.num_features    )    )
                self.assertTrue(len(backbone.out_feature_channels    ) == len(backbone.stage_names    )    )
  
  
    def 		_lowerCamelCase	(self  : Union[str, Any]    )   ->       str:
          snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()
          snake_case_						       =							inputs_dict['''pixel_values'''].shape[0]
  
          for backbone_class in self.all_model_classes:
                snake_case_						       =							backbone_class(__UpperCamelCase    )
                backbone.to(__UpperCamelCase    )
                backbone.eval()
  
                snake_case_						       =							backbone(**__UpperCamelCase    )
  
                # Test default outputs and verify feature maps
                self.assertIsInstance(outputs.feature_maps	, __UpperCamelCase    )
                self.assertTrue(len(outputs.feature_maps    ) == len(backbone.channels    )    )
                for feature_map, n_channels in zip(outputs.feature_maps	, backbone.channels    ):
                      self.assertTrue(feature_map.shape[:2]	, (batch_size, n_channels)    )
                self.assertIsNone(outputs.hidden_states    )
                self.assertIsNone(outputs.attentions    )
  
                # Test output_hidden_states=True
                snake_case_						       =							backbone(**__UpperCamelCase	, output_hidden_states=__UpperCamelCase    )
                self.assertIsNotNone(outputs.hidden_states    )
                self.assertTrue(len(outputs.hidden_states    )	, len(backbone.stage_names    )    )
                for hidden_state, n_channels in zip(outputs.hidden_states	, backbone.channels    ):
                      self.assertTrue(hidden_state.shape[:2]	, (batch_size, n_channels)    )
  
                # Test output_attentions=True
                if self.has_attentions:
                      snake_case_						       =							backbone(**__UpperCamelCase	, output_attentions=__UpperCamelCase    )
                      self.assertIsNotNone(outputs.attentions    )
  
  
style_context: 
  '''simple docstring'''
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  A           =   {
      "configuration_deta": ["DETA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetaConfig"],
  }
  
  try:
       if not is_vision_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       A           =   ["DetaImageProcessor"]
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       A           =   [
           "DETA_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DetaForObjectDetection",
           "DetaModel",
           "DetaPreTrainedModel",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig
  
       try:
            if not is_vision_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .image_processing_deta import DetaImageProcessor
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_deta import (
                DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
                DetaForObjectDetection,
                DetaModel,
                DetaPreTrainedModel,
            )
  
  else:
       import sys
  
       A           =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  