

def 	snake_case_      (__A     :						List[Any]					,   __A     :						Union[str, Any]     )			->	int:
   if n < 0:
      return 0
   # table[i] represents the number of ways to get to amount i
   __lowerCAmelCase  :			str					    =						[0] * (n + 1)

   # There is exactly 1 way to get to zero(You pick no coins).
   __lowerCAmelCase  :			List[Any]					    =						1

   # Pick all coins one by one and update table[] values
   # after the index greater than or equal to the value of the
   # picked coin
   for coin_val in s:
      for j in range(__A					,   n + 1     ):
         table[j] += table[j - coin_val]

   return table[n]


if __name__ == "__main__":
   import doctest

   doctest.testmod()





import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType



@require_torch
class SCREAMING_SNAKE_CASE	:
  """simple docstring"""






  lowerCamelCase    :			List[Any]						      =()
  lowerCamelCase    :			Union[str, Any]						      =True







  def        SCREAMING_SNAKE_CASE    (			self :    Union[str, Any]    )							->	Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :			List[Any]					    =						self.config_class

     # test default config
     __lowerCAmelCase  :			Union[str, Any]					    =						config_class()
     self.assertIsNotNone(lowerCAmelCase    )
     __lowerCAmelCase  :			Optional[int]					    =						["""stem"""] + [f'''stage{idx}''' for idx in range(1       ,							len(config.depths    ) + 1    )]
     self.assertEqual(config.stage_names       ,							lowerCAmelCase    )
     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
     __lowerCAmelCase  :			str					    =						config_class(out_features=lowerCAmelCase       ,							out_indices=lowerCAmelCase    )
     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
     __lowerCAmelCase  :			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
     __lowerCAmelCase  :			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
     __lowerCAmelCase  :			str					    =						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(lowerCAmelCase    ):
        __lowerCAmelCase  :			Dict					    =						config_class(out_features=["""stage1""", """stage2"""]       ,							out_indices=[0, 2]    )







  def        SCREAMING_SNAKE_CASE    (			self :    str    )							->	Optional[int]:






     """simple docstring"""







     __lowerCAmelCase    ,__lowerCAmelCase  :			Union[str, Any]					    =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __lowerCAmelCase  :			List[Any]					    =						model_class(lowerCAmelCase    )
        __lowerCAmelCase  :			str					    =						inspect.signature(model.forward    )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        __lowerCAmelCase  :			List[Any]					    =						[*signature.parameters.keys()]
        __lowerCAmelCase  :			Union[str, Any]					    =						["""pixel_values"""]
        self.assertListEqual(arg_names[:1]       ,							lowerCAmelCase    )







  def        SCREAMING_SNAKE_CASE    (			self :    List[Any]    )							->	Tuple:






     """simple docstring"""







     __lowerCAmelCase    ,__lowerCAmelCase  :			Dict					    =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __lowerCAmelCase  :			List[Any]					    =						model_class(lowerCAmelCase    )
        self.assertEqual(len(model.channels    )       ,							len(config.out_features    )    )
        __lowerCAmelCase  :			List[Any]					    =						model.num_features
        __lowerCAmelCase  :			List[str]					    =						[config.stage_names.index(lowerCAmelCase    ) for feat in config.out_features]
        __lowerCAmelCase  :			Union[str, Any]					    =						[num_features[idx] for idx in out_indices]
        self.assertListEqual(model.channels       ,							lowerCAmelCase    )

        __lowerCAmelCase  :			int					    =						copy.deepcopy(lowerCAmelCase    )
        __lowerCAmelCase  :			str					    =						None
        __lowerCAmelCase  :			List[Any]					    =						model_class(lowerCAmelCase    )
        self.assertEqual(len(model.channels    )       ,							1    )
        self.assertListEqual(model.channels       ,							[num_features[-1]]    )

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







  def        SCREAMING_SNAKE_CASE    (			self :    Union[str, Any]    )							->	Optional[int]:






     """simple docstring"""







     __lowerCAmelCase    ,__lowerCAmelCase  :			str					    =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __lowerCAmelCase  :			List[Any]					    =						model_class(lowerCAmelCase    )
        model.to(lowerCAmelCase    )
        model.eval()
        __lowerCAmelCase  :			str					    =						model(**lowerCAmelCase    )

        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
        __lowerCAmelCase  :			Dict					    =						copy.deepcopy(lowerCAmelCase    )
        __lowerCAmelCase  :			int					    =						None
        __lowerCAmelCase  :			Any					    =						model_class(lowerCAmelCase    )
        model.to(lowerCAmelCase    )
        model.eval()
        __lowerCAmelCase  :			Optional[int]					    =						model(**lowerCAmelCase    )

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

        __lowerCAmelCase  :			List[Any]					    =						copy.deepcopy(lowerCAmelCase    )
        __lowerCAmelCase  :			Optional[Any]					    =						None
        __lowerCAmelCase  :			Union[str, Any]					    =						model_class(lowerCAmelCase    )
        model.to(lowerCAmelCase    )
        model.eval()
        __lowerCAmelCase  :			Dict					    =						model(**lowerCAmelCase    )

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

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







  def        SCREAMING_SNAKE_CASE    (			self :    List[str]    )							->	str:






     """simple docstring"""







     __lowerCAmelCase    ,__lowerCAmelCase  :			Any					    =						self.model_tester.prepare_config_and_inputs_for_common()

     for backbone_class in self.all_model_classes:
        __lowerCAmelCase  :			List[Any]					    =						backbone_class(lowerCAmelCase    )

        self.assertTrue(hasattr(lowerCAmelCase       ,							"""backbone_type"""    )    )
        self.assertTrue(hasattr(lowerCAmelCase       ,							"""stage_names"""    )    )
        self.assertTrue(hasattr(lowerCAmelCase       ,							"""num_features"""    )    )
        self.assertTrue(hasattr(lowerCAmelCase       ,							"""out_indices"""    )    )
        self.assertTrue(hasattr(lowerCAmelCase       ,							"""out_features"""    )    )
        self.assertTrue(hasattr(lowerCAmelCase       ,							"""out_feature_channels"""    )    )
        self.assertTrue(hasattr(lowerCAmelCase       ,							"""channels"""    )    )

        self.assertIsInstance(backbone.backbone_type       ,							lowerCAmelCase    )
        # 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        SCREAMING_SNAKE_CASE    (			self :    List[str]    )							->	List[Any]:






     """simple docstring"""







     __lowerCAmelCase    ,__lowerCAmelCase  :			int					    =						self.model_tester.prepare_config_and_inputs_for_common()
     __lowerCAmelCase  :			Union[str, Any]					    =						inputs_dict["""pixel_values"""].shape[0]

     for backbone_class in self.all_model_classes:
        __lowerCAmelCase  :			Union[str, Any]					    =						backbone_class(lowerCAmelCase    )
        backbone.to(lowerCAmelCase    )
        backbone.eval()

        __lowerCAmelCase  :			Tuple					    =						backbone(**lowerCAmelCase    )

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


