



"""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   UpperCAmelCase__		:
		lowerCAmelCase_							:						Optional[int]							=  ()
		lowerCAmelCase_							:						Optional[int]							=  True



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






									'''simple docstring'''

									A    =       self.config_class

									# test default config
									A    =       config_class()
									self.assertIsNotNone(a__					)
									A    =       ['''stem'''] + [f"""stage{idx}""" for idx in range(1   ,				len(config.depths					) + 1					)]
									self.assertEqual(config.stage_names   ,				a__					)
									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    =       config_class(out_features=a__   ,				out_indices=a__					)
									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    =       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    =       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    =       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(a__					):
																A    =       config_class(out_features=['stage1', 'stage2']   ,				out_indices=[0, 2]					)



		def        A_		(			self	:     Tuple					)      ->    Optional[Any]:






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs_for_common()

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



		def        A_		(			self	:     int					)      ->    Tuple:






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs_for_common()

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

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

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



		def        A_		(			self	:     Any					)      ->    Optional[int]:






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																A    =       model_class(a__					)
																model.to(a__					)
																model.eval()
																A    =       model(**a__					)

																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    =       copy.deepcopy(a__					)
																A    =       None
																A    =       model_class(a__					)
																model.to(a__					)
																model.eval()
																A    =       model(**a__					)

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

																A    =       copy.deepcopy(a__					)
																A    =       None
																A    =       model_class(a__					)
																model.to(a__					)
																model.eval()
																A    =       model(**a__					)

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

																# Check backbone can be initialized with fresh weights
																A    =       copy.deepcopy(a__					)
																A    =       False
																A    =       model_class(a__					)
																model.to(a__					)
																model.eval()
																A    =       model(**a__					)



		def        A_		(			self	:     Dict					)      ->    List[Any]:






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs_for_common()

									for backbone_class in self.all_model_classes:
																A    =       backbone_class(a__					)

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

																self.assertIsInstance(backbone.backbone_type   ,				a__					)
																# 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]					)      ->    str:






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs_for_common()
									A    =       inputs_dict['''pixel_values'''].shape[0]

									for backbone_class in self.all_model_classes:
																A    =       backbone_class(a__					)
																backbone.to(a__					)
																backbone.eval()

																A    =       backbone(**a__					)

																# Test default outputs and verify feature maps
																self.assertIsInstance(outputs.feature_maps   ,				a__					)
																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    =       backbone(**a__   ,				output_hidden_states=a__					)
																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    =       backbone(**a__   ,				output_attentions=a__					)
																							self.assertIsNotNone(outputs.attentions					)







import os






def  _UpperCAmelCase						( )		->Tuple:


 '''simple docstring'''
 snake_case :			Dict							=   0
 snake_case :			int							=   0
 with open(os.path.dirname(__lowerCamelCase							) + '''/p022_names.txt'''							) as file:
  snake_case :			int							=   str(file.readlines()[0]							)
  snake_case :			Any							=   name.replace('''"'''			,  ''''''							).split(''','''							)

 name.sort()
 for i in range(len(__lowerCamelCase							)							):
  for j in name[i]:
   temp_sum += ord(__lowerCamelCase							) - ord('''A'''							) + 1
  total_sum += (i + 1) * temp_sum
  snake_case :			List[Any]							=   0
 return total_sum


if __name__ == "__main__":
  print(solution())






