def     lowerCamelCase_    (  _lowercase					,	_lowercase					,	_lowercase				)							->       str:
		if right <= left:
				return
		__A							:    List[Any]         =      left
		__A							:    int         =      right
		__A							:    List[Any]         =      sorting[left]
		while i <= b:
				if sorting[i] < pivot:
						__A							:    List[Any]         =      sorting[i], sorting[a]
						a += 1
						i += 1
				elif sorting[i] > pivot:
						__A							:    List[Any]         =      sorting[i], sorting[b]
						b -= 1
				else:
						i += 1
		quick_sort_apartition(A__					,	A__					,	a - 1				)
		quick_sort_apartition(A__					,	b + 1					,	A__				)



def     lowerCamelCase_    (  _lowercase					,	_lowercase					,	_lowercase				)							->       Any:
		if left < right:
				__A							:    Optional[int]         =      lomuto_partition(A__					,	A__					,	A__				)
				quick_sort_lomuto_partition(A__					,	A__					,	pivot_index - 1				)
				quick_sort_lomuto_partition(A__					,	pivot_index + 1					,	A__				)



def     lowerCamelCase_    (  _lowercase					,	_lowercase					,	_lowercase				)							->       Optional[Any]:
		__A							:    Dict         =      sorting[right]
		__A							:    List[str]         =      left
		for i in range(A__					,	A__				):
				if sorting[i] < pivot:
						__A							:    int         =      sorting[i], sorting[store_index]
						store_index += 1
		__A							:    List[str]         =      sorting[store_index], sorting[right]
		return store_index



def     lowerCamelCase_    (  _lowercase				)							->       List[str]:
		if len(A__				) <= 1:
				return sorting
		return (
		    three_way_radix_quicksort([i for i in sorting if i < sorting[0]]				)
		    + [i for i in sorting if i == sorting[0]]
		    + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]				)
		)


if __name__ == "__main__":
					import doctest

					doctest.testmod(verbose=True)

					UpperCamelCase			 =	input('Enter numbers separated by a comma:\n').strip()
					UpperCamelCase			 =	[int(item) for item in user_input.split(',')]
					quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
					print(unsorted)









import copy
import inspect

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







@require_torch
class      __lowercase  :





							"""simple docstring"""







							_UpperCAmelCase     =		()
							_UpperCAmelCase     =		True






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= self.config_class

										# test default config
										SCREAMING_SNAKE_CASE_						:			Tuple       						= config_class()
										self.assertIsNotNone(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Dict       						= ['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
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= 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
										SCREAMING_SNAKE_CASE_						:			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
										SCREAMING_SNAKE_CASE_						:			List[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
										SCREAMING_SNAKE_CASE_						:			List[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(lowerCAmelCase__    ):
													SCREAMING_SNAKE_CASE_						:			str       						= config_class(out_features=['stage1', 'stage2']       ,		out_indices=[0, 2]    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			Tuple       						= self.model_tester.prepare_config_and_inputs_for_common()

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






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




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

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

													SCREAMING_SNAKE_CASE_						:			Tuple       						= copy.deepcopy(lowerCAmelCase__    )
													SCREAMING_SNAKE_CASE_						:			Dict       						= None
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model_class(lowerCAmelCase__    )
													self.assertEqual(len(model.channels    )       ,		1    )
													self.assertListEqual(model.channels       ,		[num_features[-1]]    )

													SCREAMING_SNAKE_CASE_						:			Dict       						= copy.deepcopy(lowerCAmelCase__    )
													SCREAMING_SNAKE_CASE_						:			Tuple       						= None
													SCREAMING_SNAKE_CASE_						:			int       						= model_class(lowerCAmelCase__    )
													self.assertEqual(len(model.channels    )       ,		1    )
													self.assertListEqual(model.channels       ,		[num_features[-1]]    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model_class(lowerCAmelCase__    )
													model.to(lowerCAmelCase__    )
													model.eval()
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= 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
													SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= copy.deepcopy(lowerCAmelCase__    )
													SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= None
													SCREAMING_SNAKE_CASE_						:			int       						= model_class(lowerCAmelCase__    )
													model.to(lowerCAmelCase__    )
													model.eval()
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= model(**lowerCAmelCase__    )

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

													SCREAMING_SNAKE_CASE_						:			Tuple       						= copy.deepcopy(lowerCAmelCase__    )
													SCREAMING_SNAKE_CASE_						:			int       						= None
													SCREAMING_SNAKE_CASE_						:			Dict       						= model_class(lowerCAmelCase__    )
													model.to(lowerCAmelCase__    )
													model.eval()
													SCREAMING_SNAKE_CASE_						:			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
													SCREAMING_SNAKE_CASE_						:			Tuple       						= copy.deepcopy(lowerCAmelCase__    )
													SCREAMING_SNAKE_CASE_						:			Dict       						= False
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model_class(lowerCAmelCase__    )
													model.to(lowerCAmelCase__    )
													model.eval()
													SCREAMING_SNAKE_CASE_						:			str       						= model(**lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= self.model_tester.prepare_config_and_inputs_for_common()

										for backbone_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_						:			List[str]       						= 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    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			List[str]       						= self.model_tester.prepare_config_and_inputs_for_common()
										SCREAMING_SNAKE_CASE_						:			Tuple       						= inputs_dict['pixel_values'].shape[0]

										for backbone_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= backbone_class(lowerCAmelCase__    )
													backbone.to(lowerCAmelCase__    )
													backbone.eval()

													SCREAMING_SNAKE_CASE_						:			Dict       						= 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
													SCREAMING_SNAKE_CASE_						:			List[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:
																SCREAMING_SNAKE_CASE_						:			Tuple       						= backbone(**lowerCAmelCase__       ,		output_attentions=lowerCAmelCase__    )
																self.assertIsNotNone(outputs.attentions    )





