




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"""

      lowerCAmelCase__     :	int      =   ()
      lowerCAmelCase__     :	List[Any]      =   True







      def  snake_case		(       self   :	List[Any]			):
          __UpperCamelCase											=  self.config_class

          # test default config
          __UpperCamelCase											=  config_class()
          self.assertIsNotNone(__SCREAMING_SNAKE_CASE			)
          __UpperCamelCase											=  ['''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
          __UpperCamelCase											=  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
          __UpperCamelCase											=  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
          __UpperCamelCase											=  config_class(out_features=['''stage1''', '''stage3''']			)
          self.assertEqual(config.out_features	,    ['''stage1''', '''stage3''']			)
          self.assertEqual(config.out_indices	,    [1, 3]			)

          # Only out_indices set
          __UpperCamelCase											=  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			):
              __UpperCamelCase											=  config_class(out_features=['''stage1''', '''stage2''']	,    out_indices=[0, 2]			)







      def  snake_case		(       self   :	Optional[Any]			):
          __UpperCamelCase						, __UpperCamelCase											=  self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              __UpperCamelCase											=  model_class(__SCREAMING_SNAKE_CASE			)
              __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]	,    __SCREAMING_SNAKE_CASE			)







      def  snake_case		(       self   :	Optional[int]			):
          __UpperCamelCase						, __UpperCamelCase											=  self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              __UpperCamelCase											=  model_class(__SCREAMING_SNAKE_CASE			)
              self.assertEqual(len(model.channels			)	,    len(config.out_features			)			)
              __UpperCamelCase											=  model.num_features
              __UpperCamelCase											=  [config.stage_names.index(__SCREAMING_SNAKE_CASE			) for feat in config.out_features]
              __UpperCamelCase											=  [num_features[idx] for idx in out_indices]
              self.assertListEqual(model.channels	,    __SCREAMING_SNAKE_CASE			)

              __UpperCamelCase											=  copy.deepcopy(__SCREAMING_SNAKE_CASE			)
              __UpperCamelCase											=  None
              __UpperCamelCase											=  model_class(__SCREAMING_SNAKE_CASE			)
              self.assertEqual(len(model.channels			)	,    1			)
              self.assertListEqual(model.channels	,    [num_features[-1]]			)

              __UpperCamelCase											=  copy.deepcopy(__SCREAMING_SNAKE_CASE			)
              __UpperCamelCase											=  None
              __UpperCamelCase											=  model_class(__SCREAMING_SNAKE_CASE			)
              self.assertEqual(len(model.channels			)	,    1			)
              self.assertListEqual(model.channels	,    [num_features[-1]]			)







      def  snake_case		(       self   :	str			):
          __UpperCamelCase						, __UpperCamelCase											=  self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              __UpperCamelCase											=  model_class(__SCREAMING_SNAKE_CASE			)
              model.to(__SCREAMING_SNAKE_CASE			)
              model.eval()
              __UpperCamelCase											=  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
              __UpperCamelCase											=  copy.deepcopy(__SCREAMING_SNAKE_CASE			)
              __UpperCamelCase											=  None
              __UpperCamelCase											=  model_class(__SCREAMING_SNAKE_CASE			)
              model.to(__SCREAMING_SNAKE_CASE			)
              model.eval()
              __UpperCamelCase											=  model(**__SCREAMING_SNAKE_CASE			)

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

              __UpperCamelCase											=  copy.deepcopy(__SCREAMING_SNAKE_CASE			)
              __UpperCamelCase											=  None
              __UpperCamelCase											=  model_class(__SCREAMING_SNAKE_CASE			)
              model.to(__SCREAMING_SNAKE_CASE			)
              model.eval()
              __UpperCamelCase											=  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
              __UpperCamelCase											=  copy.deepcopy(__SCREAMING_SNAKE_CASE			)
              __UpperCamelCase											=  False
              __UpperCamelCase											=  model_class(__SCREAMING_SNAKE_CASE			)
              model.to(__SCREAMING_SNAKE_CASE			)
              model.eval()
              __UpperCamelCase											=  model(**__SCREAMING_SNAKE_CASE			)







      def  snake_case		(       self   :	Tuple			):
          __UpperCamelCase						, __UpperCamelCase											=  self.model_tester.prepare_config_and_inputs_for_common()

          for backbone_class in self.all_model_classes:
              __UpperCamelCase											=  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  snake_case		(       self   :	Optional[Any]			):
          __UpperCamelCase						, __UpperCamelCase											=  self.model_tester.prepare_config_and_inputs_for_common()
          __UpperCamelCase											=  inputs_dict['''pixel_values'''].shape[0]

          for backbone_class in self.all_model_classes:
              __UpperCamelCase											=  backbone_class(__SCREAMING_SNAKE_CASE			)
              backbone.to(__SCREAMING_SNAKE_CASE			)
              backbone.eval()

              __UpperCamelCase											=  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
              __UpperCamelCase											=  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:
                  __UpperCamelCase											=  backbone(**__SCREAMING_SNAKE_CASE	,    output_attentions=__SCREAMING_SNAKE_CASE			)
                  self.assertIsNotNone(outputs.attentions			)












def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		int:

    """simple docstring"""


    if not isinstance(lowercase_   ,       lowercase_ ):
        raise TypeError('''recursive_lucas_number accepts only integer arguments.''' )
    if n_th_number == 0:
        return 2
    if n_th_number == 1:
        return 1

    return recursive_lucas_number(n_th_number - 1 ) + recursive_lucas_number(
        n_th_number - 2 )




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		int:

    """simple docstring"""


    if not isinstance(lowercase_   ,       lowercase_ ):
        raise TypeError('''dynamic_lucas_number accepts only integer arguments.''' )
    __UpperCamelCase						, __UpperCamelCase											=  2, 1
    for _ in range(lowercase_ ):
        __UpperCamelCase						, __UpperCamelCase											=  b, a + b
    return a


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 a_										=       int(input("Enter the number of terms in lucas series:\n").strip())
 print("Using recursive function to calculate lucas series:")
 print(" ".join(str(recursive_lucas_number(i)) for i in range(n)))
 print("\nUsing dynamic function to calculate lucas series:")
 print(" ".join(str(dynamic_lucas_number(i)) for i in range(n)))






