

import copy
import inspect

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

@require_torch
class __UpperCamelCase    :






					'''simple docstring'''



					__magic_name__     			=      ()
					__magic_name__     			=      True





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Any		       =   self.config_class

						# test default config
						UpperCAmelCase__:  Any		       =   config_class()
						self.assertIsNotNone(lowerCamelCase__	)
						UpperCAmelCase__:  List[Any]		       =   ["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
						UpperCAmelCase__:  Dict		       =   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
						UpperCAmelCase__:  Dict		       =   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__:  str		       =   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__:  Dict		       =   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__	):
							UpperCAmelCase__:  Union[str, Any]		       =   config_class(out_features=["stage1", "stage2"]							,			out_indices=[0, 2]	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__       ,	UpperCAmelCase__:  Optional[int]		       =   self.model_tester.prepare_config_and_inputs_for_common()

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





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__       ,	UpperCAmelCase__:  Any		       =   self.model_tester.prepare_config_and_inputs_for_common()

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

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

							UpperCAmelCase__:  Dict		       =   copy.deepcopy(lowerCamelCase__	)
							UpperCAmelCase__:  Any		       =   None
							UpperCAmelCase__:  str		       =   model_class(lowerCamelCase__	)
							self.assertEqual(len(model.channels	)							,			1	)
							self.assertListEqual(model.channels							,			[num_features[-1]]	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__       ,	UpperCAmelCase__:  Union[str, Any]		       =   self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							UpperCAmelCase__:  List[Any]		       =   model_class(lowerCamelCase__	)
							model.to(lowerCamelCase__	)
							model.eval()
							UpperCAmelCase__:  int		       =   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
							UpperCAmelCase__:  Any		       =   copy.deepcopy(lowerCamelCase__	)
							UpperCAmelCase__:  List[Any]		       =   None
							UpperCAmelCase__:  Optional[Any]		       =   model_class(lowerCamelCase__	)
							model.to(lowerCamelCase__	)
							model.eval()
							UpperCAmelCase__:  Optional[int]		       =   model(**lowerCamelCase__	)

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

							UpperCAmelCase__:  Any		       =   copy.deepcopy(lowerCamelCase__	)
							UpperCAmelCase__:  Tuple		       =   None
							UpperCAmelCase__:  Dict		       =   model_class(lowerCamelCase__	)
							model.to(lowerCamelCase__	)
							model.eval()
							UpperCAmelCase__:  List[str]		       =   model(**lowerCamelCase__	)

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

							# Check backbone can be initialized with fresh weights
							UpperCAmelCase__:  Optional[Any]		       =   copy.deepcopy(lowerCamelCase__	)
							UpperCAmelCase__:  Any		       =   False
							UpperCAmelCase__:  Union[str, Any]		       =   model_class(lowerCamelCase__	)
							model.to(lowerCamelCase__	)
							model.eval()
							UpperCAmelCase__:  Dict		       =   model(**lowerCamelCase__	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__       ,	UpperCAmelCase__:  int		       =   self.model_tester.prepare_config_and_inputs_for_common()

						for backbone_class in self.all_model_classes:
							UpperCAmelCase__:  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       _UpperCAmelCase						(							self	):
						UpperCAmelCase__       ,	UpperCAmelCase__:  int		       =   self.model_tester.prepare_config_and_inputs_for_common()
						UpperCAmelCase__:  Union[str, Any]		       =   inputs_dict["pixel_values"].shape[0]

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

							UpperCAmelCase__:  List[Any]		       =   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
							UpperCAmelCase__:  Optional[int]		       =   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:
								UpperCAmelCase__:  Union[str, Any]		       =   backbone(**lowerCamelCase__							,			output_attentions=lowerCamelCase__	)
								self.assertIsNotNone(outputs.attentions	)


_lowerCAmelCase     :			Optional[int]    ={
    """+""": lambda x, y: x + y,
    """-""": lambda x, y: x - y,
    """*""": lambda x, y: x * y,
    """/""": lambda x, y: x / y,
}



def  _A  (			SCREAMING_SNAKE_CASE			):
	return c.isdigit()



def  _A  (			SCREAMING_SNAKE_CASE			):
	UpperCAmelCase__:  str		       =   []

	# iterate over the string in reverse order
	for c in expression.split()[::-1]:
		# push operand to stack
		if is_operand(SCREAMING_SNAKE_CASE			):
			stack.append(int(SCREAMING_SNAKE_CASE			)			)

		else:
			# pop values from stack can calculate the result
			# push the result onto the stack again
			UpperCAmelCase__:  Any		       =   stack.pop()
			UpperCAmelCase__:  int		       =   stack.pop()
			stack.append(calc[c](SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)			)

	return stack.pop()


# Driver code
if __name__ == "__main__":
					_lowerCAmelCase     :			Optional[int]    ="""+ 9 * 2 6"""
					print(evaluate(test_expression))

					_lowerCAmelCase     :			Optional[Any]    ="""/ * 10 2 + 4 1 """
					print(evaluate(test_expression))