


'''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"""
    a													=    ()
    a													=    True


    def    A    (  self			:							str):
           _A     :	Tuple      =					self.config_class

           # test default config
           _A     :	str      =					config_class()
           self.assertIsNotNone(SCREAMING_SNAKE_CASE)
           _A     :	int      =					['stem'] + [F'stage{idx}' for idx in range(1      ,							len(config.depths) + 1)]
           self.assertEqual(config.stage_names      ,							SCREAMING_SNAKE_CASE)
           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
           _A     :	Dict      =					config_class(out_features=SCREAMING_SNAKE_CASE      ,							out_indices=SCREAMING_SNAKE_CASE)
           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
           _A     :	List[str]      =					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
           _A     :	Union[str, Any]      =					config_class(out_features=['stage1', 'stage3'])
           self.assertEqual(config.out_features      ,							['stage1', 'stage3'])
           self.assertEqual(config.out_indices      ,							[1, 3])

           # Only out_indices set
           _A     :	Any      =					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(SCREAMING_SNAKE_CASE):
                  _A     :	Optional[int]      =					config_class(out_features=['stage1', 'stage2']      ,							out_indices=[0, 2])


    def    A    (  self			:							Optional[Any]):
           _A  , _A     :	Dict      =					self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  _A     :	Dict      =					model_class(SCREAMING_SNAKE_CASE)
                  _A     :	List[Any]      =					inspect.signature(model.forward)
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  _A     :	Optional[Any]      =					[*signature.parameters.keys()]
                  _A     :	Optional[Any]      =					['pixel_values']
                  self.assertListEqual(arg_names[:1]      ,							SCREAMING_SNAKE_CASE)


    def    A    (  self			:							str):
           _A  , _A     :	Union[str, Any]      =					self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  _A     :	int      =					model_class(SCREAMING_SNAKE_CASE)
                  self.assertEqual(len(model.channels)      ,							len(config.out_features))
                  _A     :	Tuple      =					model.num_features
                  _A     :	List[Any]      =					[config.stage_names.index(SCREAMING_SNAKE_CASE) for feat in config.out_features]
                  _A     :	Optional[Any]      =					[num_features[idx] for idx in out_indices]
                  self.assertListEqual(model.channels      ,							SCREAMING_SNAKE_CASE)

                  _A     :	List[Any]      =					copy.deepcopy(SCREAMING_SNAKE_CASE)
                  _A     :	List[Any]      =					None
                  _A     :	Any      =					model_class(SCREAMING_SNAKE_CASE)
                  self.assertEqual(len(model.channels)      ,							1)
                  self.assertListEqual(model.channels      ,							[num_features[-1]])

                  _A     :	str      =					copy.deepcopy(SCREAMING_SNAKE_CASE)
                  _A     :	Any      =					None
                  _A     :	Tuple      =					model_class(SCREAMING_SNAKE_CASE)
                  self.assertEqual(len(model.channels)      ,							1)
                  self.assertListEqual(model.channels      ,							[num_features[-1]])


    def    A    (  self			:							Optional[Any]):
           _A  , _A     :	Any      =					self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  _A     :	str      =					model_class(SCREAMING_SNAKE_CASE)
                  model.to(SCREAMING_SNAKE_CASE)
                  model.eval()
                  _A     :	int      =					model(**SCREAMING_SNAKE_CASE)

                  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
                  _A     :	Tuple      =					copy.deepcopy(SCREAMING_SNAKE_CASE)
                  _A     :	Any      =					None
                  _A     :	Any      =					model_class(SCREAMING_SNAKE_CASE)
                  model.to(SCREAMING_SNAKE_CASE)
                  model.eval()
                  _A     :	Dict      =					model(**SCREAMING_SNAKE_CASE)

                  self.assertEqual(len(result.feature_maps)      ,							1)
                  self.assertEqual(len(model.channels)      ,							1)

                  _A     :	str      =					copy.deepcopy(SCREAMING_SNAKE_CASE)
                  _A     :	Dict      =					None
                  _A     :	Tuple      =					model_class(SCREAMING_SNAKE_CASE)
                  model.to(SCREAMING_SNAKE_CASE)
                  model.eval()
                  _A     :	List[str]      =					model(**SCREAMING_SNAKE_CASE)

                  self.assertEqual(len(result.feature_maps)      ,							1)
                  self.assertEqual(len(model.channels)      ,							1)

                  # Check backbone can be initialized with fresh weights
                  _A     :	List[Any]      =					copy.deepcopy(SCREAMING_SNAKE_CASE)
                  _A     :	str      =					False
                  _A     :	Union[str, Any]      =					model_class(SCREAMING_SNAKE_CASE)
                  model.to(SCREAMING_SNAKE_CASE)
                  model.eval()
                  _A     :	int      =					model(**SCREAMING_SNAKE_CASE)


    def    A    (  self			:							List[str]):
           _A  , _A     :	Optional[int]      =					self.model_tester.prepare_config_and_inputs_for_common()

           for backbone_class in self.all_model_classes:
                  _A     :	int      =					backbone_class(SCREAMING_SNAKE_CASE)

                  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,							'backbone_type'))
                  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,							'stage_names'))
                  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,							'num_features'))
                  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,							'out_indices'))
                  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,							'out_features'))
                  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,							'out_feature_channels'))
                  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE      ,							'channels'))

                  self.assertIsInstance(backbone.backbone_type      ,							SCREAMING_SNAKE_CASE)
                  # 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    A    (  self			:							List[str]):
           _A  , _A     :	Any      =					self.model_tester.prepare_config_and_inputs_for_common()
           _A     :	Any      =					inputs_dict['pixel_values'].shape[0]

           for backbone_class in self.all_model_classes:
                  _A     :	Any      =					backbone_class(SCREAMING_SNAKE_CASE)
                  backbone.to(SCREAMING_SNAKE_CASE)
                  backbone.eval()

                  _A     :	Optional[Any]      =					backbone(**SCREAMING_SNAKE_CASE)

                  # Test default outputs and verify feature maps
                  self.assertIsInstance(outputs.feature_maps      ,							SCREAMING_SNAKE_CASE)
                  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
                  _A     :	Any      =					backbone(**SCREAMING_SNAKE_CASE      ,							output_hidden_states=SCREAMING_SNAKE_CASE)
                  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:
                         _A     :	List[Any]      =					backbone(**SCREAMING_SNAKE_CASE      ,							output_attentions=SCREAMING_SNAKE_CASE)
                         self.assertIsNotNone(outputs.attentions)










'''simple docstring'''







import math






def       lowerCAmelCase__       (				lowerCamelCase     :			int   ):
       _A     :	int      =					math.log(2  ,10   ) * number
       _A     :	List[Any]      =					round((log_number - int(lowerCamelCase   ))  ,15   )

       return difference






def       lowerCAmelCase__       (				lowerCamelCase     :			int = 678910   ):
       _A     :	List[str]      =					90
       _A     :	List[Any]      =					0

       _A     :	List[str]      =					math.log(1.23  ,10   )
       _A     :	Any      =					math.log(1.24  ,10   )
       _A     :	Union[str, Any]      =					0

       while position < number:
              _A     :	Dict      =					log_difference(lowerCamelCase   )

              if difference >= upper_limit:
                     power_iterator += 93

              elif difference < lower_limit:
                     power_iterator += 196

              else:
                     _A     :	str      =					power_iterator
                     power_iterator += 196
                     position += 1

       return previous_power


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   print(f"""{solution() = }""")






