import copy
import inspect

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





@require_torch
class 					__snake_case    :
					__lowerCamelCase			   =  ()
					__lowerCamelCase			   =  True




					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									snake_case__   :      List[str]   				= self.config_class

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




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



									'''simple docstring'''







									snake_case__				,    snake_case__   :      List[Any]   				= self.model_tester.prepare_config_and_inputs_for_common()

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




					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







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

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

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

													snake_case__   :      Any   				= copy.deepcopy(__UpperCamelCase						)
													snake_case__   :      List[Any]   				= None
													snake_case__   :      Tuple   				= model_class(__UpperCamelCase						)
													self.assertEqual(len(model.channels						)       ,			1						)
													self.assertListEqual(model.channels       ,			[num_features[-1]]						)




					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







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

									for model_class in self.all_model_classes:
													snake_case__   :      Union[str, Any]   				= model_class(__UpperCamelCase						)
													model.to(__UpperCamelCase						)
													model.eval()
													snake_case__   :      Tuple   				= model(**__UpperCamelCase						)

													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
													snake_case__   :      int   				= copy.deepcopy(__UpperCamelCase						)
													snake_case__   :      int   				= None
													snake_case__   :      Dict   				= model_class(__UpperCamelCase						)
													model.to(__UpperCamelCase						)
													model.eval()
													snake_case__   :      Optional[int]   				= model(**__UpperCamelCase						)

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

													snake_case__   :      Optional[Any]   				= copy.deepcopy(__UpperCamelCase						)
													snake_case__   :      str   				= None
													snake_case__   :      List[str]   				= model_class(__UpperCamelCase						)
													model.to(__UpperCamelCase						)
													model.eval()
													snake_case__   :      Tuple   				= model(**__UpperCamelCase						)

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

													# Check backbone can be initialized with fresh weights
													snake_case__   :      Optional[Any]   				= copy.deepcopy(__UpperCamelCase						)
													snake_case__   :      int   				= False
													snake_case__   :      List[Any]   				= model_class(__UpperCamelCase						)
													model.to(__UpperCamelCase						)
													model.eval()
													snake_case__   :      str   				= model(**__UpperCamelCase						)




					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







									snake_case__				,    snake_case__   :      Optional[int]   				= self.model_tester.prepare_config_and_inputs_for_common()

									for backbone_class in self.all_model_classes:
													snake_case__   :      List[str]   				= backbone_class(__UpperCamelCase						)

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

													self.assertIsInstance(backbone.backbone_type       ,			__UpperCamelCase						)
													# 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						)	->    Dict:



									'''simple docstring'''







									snake_case__				,    snake_case__   :      str   				= self.model_tester.prepare_config_and_inputs_for_common()
									snake_case__   :      Optional[Any]   				= inputs_dict['pixel_values'].shape[0]

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

													snake_case__   :      int   				= backbone(**__UpperCamelCase						)

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









"""simple docstring"""
from __future__ import annotations

from queue import Queue






def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							a ^= b
							b ^= a
							a ^= b
							return a, b






def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							A						=    1
							while (1 << j) < max_node:
														for i in range(1   ,    max_node + 1			):
																					A						=    parent[j - 1][parent[j - 1][i]]
														j += 1
							return parent






def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			):
							# u must be deeper in the tree than v
							if level[u] < level[v]:
														A,      A						=    swap(UpperCamelCase   ,    UpperCamelCase			)
							# making depth of u same as depth of v
							for i in range(18   ,    -1   ,    -1			):
														if level[u] - (1 << i) >= level[v]:
																					A						=    parent[i][u]
    # at the same depth if u==v that mean lca is found
							if u == v:
														return u
							# moving both nodes upwards till lca in found
							for i in range(18   ,    -1   ,    -1			):
														if parent[i][u] not in [0, parent[i][v]]:
																					A,      A						=    parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
							return parent[0][u]






def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase = 1   ,    ):
							A						=    0
							A						=    Queue(maxsize=UpperCamelCase			)
							q.put(UpperCamelCase			)
							while q.qsize() != 0:
														A						=    q.get()
														for v in graph[u]:
																					if level[v] == -1:
																												A						=    level[u] + 1
																												q.put(UpperCamelCase			)
																												A						=    u
							return level, parent






def    A__							(     ):
							A						=    13
							# initializing with 0
							A						=    [[0 for _ in range(max_node + 10			)] for _ in range(20			)]
							# initializing with -1 which means every node is unvisited
							A						=    [-1 for _ in range(max_node + 10			)]
							A						=    {
							    1: [2, 3, 4],
							    2: [5],
							    3: [6, 7],
							    4: [8],
							    5: [9, 10],
							    6: [11],
							    7: [],
							    8: [12, 13],
							    9: [],
							    10: [],
							    11: [],
							    12: [],
							    13: [],
							}
							A,      A						=    breadth_first_search(UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    1			)
							A						=    create_sparse(UpperCamelCase   ,    UpperCamelCase			)
							print("LCA of node 1 and 3 is: "   ,    lowest_common_ancestor(1   ,    3   ,    UpperCamelCase   ,    UpperCamelCase			)			)
							print("LCA of node 5 and 6 is: "   ,    lowest_common_ancestor(5   ,    6   ,    UpperCamelCase   ,    UpperCamelCase			)			)
							print("LCA of node 7 and 11 is: "   ,    lowest_common_ancestor(7   ,    11   ,    UpperCamelCase   ,    UpperCamelCase			)			)
							print("LCA of node 6 and 7 is: "   ,    lowest_common_ancestor(6   ,    7   ,    UpperCamelCase   ,    UpperCamelCase			)			)
							print("LCA of node 4 and 12 is: "   ,    lowest_common_ancestor(4   ,    12   ,    UpperCamelCase   ,    UpperCamelCase			)			)
							print("LCA of node 8 and 8 is: "   ,    lowest_common_ancestor(8   ,    8   ,    UpperCamelCase   ,    UpperCamelCase			)			)


if __name__ == "__main__":
		main()


