


import argparse
import copy

def 			lowerCAmelCase__					(     a__:						Tuple			)    ->						Any:


							'''simple docstring'''





							_UpperCAmelCase											=     {}

							with open(a__			) as f:
														for line in f:
																					if line.split()[0] not in dict_of_neighbours:
																												_UpperCAmelCase											=     []
																												_list.append([line.split()[1], line.split()[2]]			)
																												_UpperCAmelCase											=     _list
																					else:
																												dict_of_neighbours[line.split()[0]].append(
																												    [line.split()[1], line.split()[2]]			)
																					if line.split()[1] not in dict_of_neighbours:
																												_UpperCAmelCase											=     []
																												_list.append([line.split()[0], line.split()[2]]			)
																												_UpperCAmelCase											=     _list
																					else:
																												dict_of_neighbours[line.split()[1]].append(
																												    [line.split()[0], line.split()[2]]			)

							return dict_of_neighbours

def 			lowerCAmelCase__					(     a__:						Dict     ,    a__:						List[Any]			)    ->						List[str]:


							'''simple docstring'''





							with open(a__			) as f:
														_UpperCAmelCase											=     f.read(1			)
							_UpperCAmelCase											=     start_node

							_UpperCAmelCase											=     []

							_UpperCAmelCase											=     start_node

							_UpperCAmelCase											=     0
							while visiting not in first_solution:
														_UpperCAmelCase											=     1_0_0_0_0
														for k in dict_of_neighbours[visiting]:
																					if int(k[1]			) < int(a__			) and k[0] not in first_solution:
																												_UpperCAmelCase											=     k[1]
																												_UpperCAmelCase											=     k[0]

														first_solution.append(a__			)
														_UpperCAmelCase											=     distance_of_first_solution + int(a__			)
														_UpperCAmelCase											=     best_node

							first_solution.append(a__			)

							_UpperCAmelCase											=     0
							for k in dict_of_neighbours[first_solution[-2]]:
														if k[0] == start_node:
																					break
														position += 1

							_UpperCAmelCase											=     (
							    distance_of_first_solution
							    + int(dict_of_neighbours[first_solution[-2]][position][1]			)
							    - 1_0_0_0_0
							)
							return first_solution, distance_of_first_solution

def 			lowerCAmelCase__					(     a__:						List[Any]     ,    a__:						Tuple			)    ->						List[str]:


							'''simple docstring'''





							_UpperCAmelCase											=     []

							for n in solution[1:-1]:
														_UpperCAmelCase											=     solution.index(a__			)
														for kn in solution[1:-1]:
																					_UpperCAmelCase											=     solution.index(a__			)
																					if n == kn:
																												continue

																					_UpperCAmelCase											=     copy.deepcopy(a__			)
																					_UpperCAmelCase											=     kn
																					_UpperCAmelCase											=     n

																					_UpperCAmelCase											=     0

																					for k in _tmp[:-1]:
																												_UpperCAmelCase											=     _tmp[_tmp.index(a__			) + 1]
																												for i in dict_of_neighbours[k]:
																																			if i[0] == next_node:
																																										_UpperCAmelCase											=     distance + int(i[1]			)
																					_tmp.append(a__			)

																					if _tmp not in neighborhood_of_solution:
																												neighborhood_of_solution.append(_tmp			)

							_UpperCAmelCase											=     len(neighborhood_of_solution[0]			) - 1

							neighborhood_of_solution.sort(key=lambda a__			: x[index_of_last_item_in_the_list]			)
							return neighborhood_of_solution

def 			lowerCAmelCase__					(     a__:						List[Any]     ,    a__:						Optional[Any]     ,    a__:						Optional[int]     ,    a__:						int     ,    a__:						Optional[int]			)    ->						str:


							'''simple docstring'''





							_UpperCAmelCase											=     1
							_UpperCAmelCase											=     first_solution
							_UpperCAmelCase											=     []
							_UpperCAmelCase											=     distance_of_first_solution
							_UpperCAmelCase											=     solution

							while count <= iters:
														_UpperCAmelCase											=     find_neighborhood(a__     ,    a__			)
														_UpperCAmelCase											=     0
														_UpperCAmelCase											=     neighborhood[index_of_best_solution]
														_UpperCAmelCase											=     len(a__			) - 1

														_UpperCAmelCase											=     False
														while not found:
																					_UpperCAmelCase											=     0
																					while i < len(a__			):
																												if best_solution[i] != solution[i]:
																																			_UpperCAmelCase											=     best_solution[i]
																																			_UpperCAmelCase											=     solution[i]
																																			break
																												_UpperCAmelCase											=     i + 1

																					if [first_exchange_node, second_exchange_node] not in tabu_list and [
																					    second_exchange_node,
																					    first_exchange_node,
																					] not in tabu_list:
																												tabu_list.append([first_exchange_node, second_exchange_node]			)
																												_UpperCAmelCase											=     True
																												_UpperCAmelCase											=     best_solution[:-1]
																												_UpperCAmelCase											=     neighborhood[index_of_best_solution][best_cost_index]
																												if cost < best_cost:
																																			_UpperCAmelCase											=     cost
																																			_UpperCAmelCase											=     solution
																					else:
																												_UpperCAmelCase											=     index_of_best_solution + 1
																												_UpperCAmelCase											=     neighborhood[index_of_best_solution]

														if len(a__			) >= size:
																					tabu_list.pop(0			)

														_UpperCAmelCase											=     count + 1

							return best_solution_ever, best_cost

def 			lowerCAmelCase__					(     a__:						int=None			)    ->						int:


							'''simple docstring'''





							_UpperCAmelCase											=     generate_neighbours(args.File			)

							_UpperCAmelCase						,			_UpperCAmelCase											=     generate_first_solution(
							    args.File     ,    a__			)

							_UpperCAmelCase						,			_UpperCAmelCase											=     tabu_search(
							    a__     ,    a__     ,    a__     ,    args.Iterations     ,    args.Size     ,    )

							print(F'''Best solution: {best_sol}, with total distance: {best_cost}.'''			)


if __name__ == "__main__":
		lowerCAmelCase__    :str       =				argparse.ArgumentParser(description='''Tabu Search''')
		parser.add_argument(
		    '''-f''',
		    '''--File''',
		    type=str,
		    help='''Path to the file containing the data''',
		    required=True,
		)
		parser.add_argument(
		    '''-i''',
		    '''--Iterations''',
		    type=int,
		    help='''How many iterations the algorithm should perform''',
		    required=True,
		)
		parser.add_argument(
		    '''-s''', '''--Size''', type=int, help='''Size of the tabu list''', required=True
		)

		# Pass the arguments to main method
		main(parser.parse_args())




import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
		import jax

		from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel

class 						__a (							unittest.TestCase     ):
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE=13     ,       _SCREAMING_SNAKE_CASE=30     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE=3     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=32     ,       _SCREAMING_SNAKE_CASE=5     ,       _SCREAMING_SNAKE_CASE=4     ,       _SCREAMING_SNAKE_CASE=37     ,       _SCREAMING_SNAKE_CASE="gelu"     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=10     ,       _SCREAMING_SNAKE_CASE=0.02     ,       )					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     parent
									_UpperCAmelCase											=     batch_size
									_UpperCAmelCase											=     image_size
									_UpperCAmelCase											=     patch_size
									_UpperCAmelCase											=     num_channels
									_UpperCAmelCase											=     is_training
									_UpperCAmelCase											=     use_labels
									_UpperCAmelCase											=     hidden_size
									_UpperCAmelCase											=     num_hidden_layers
									_UpperCAmelCase											=     num_attention_heads
									_UpperCAmelCase											=     intermediate_size
									_UpperCAmelCase											=     hidden_act
									_UpperCAmelCase											=     hidden_dropout_prob
									_UpperCAmelCase											=     attention_probs_dropout_prob
									_UpperCAmelCase											=     type_sequence_label_size
									_UpperCAmelCase											=     initializer_range

									# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
									_UpperCAmelCase											=     (image_size // patch_size) ** 2
									_UpperCAmelCase											=     num_patches + 1
		def       UpperCAmelCase__							(     self		)					->			Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase											=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

									_UpperCAmelCase											=     ViTConfig(
									    image_size=self.image_size     ,       patch_size=self.patch_size     ,       num_channels=self.num_channels     ,       hidden_size=self.hidden_size     ,       num_hidden_layers=self.num_hidden_layers     ,       num_attention_heads=self.num_attention_heads     ,       intermediate_size=self.intermediate_size     ,       hidden_act=self.hidden_act     ,       hidden_dropout_prob=self.hidden_dropout_prob     ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,       is_decoder=_SCREAMING_SNAKE_CASE     ,       initializer_range=self.initializer_range     ,       )

									return config, pixel_values
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			str:




									"""simple docstring"""





									_UpperCAmelCase											=     FlaxViTModel(config=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE		)
									# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
									_UpperCAmelCase											=     (self.image_size, self.image_size)
									_UpperCAmelCase											=     (self.patch_size, self.patch_size)
									_UpperCAmelCase											=     (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
									self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, num_patches + 1, self.hidden_size)		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     self.type_sequence_label_size
									_UpperCAmelCase											=     FlaxViTForImageClassification(config=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE		)
									self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.type_sequence_label_size)		)

									# test greyscale images
									_UpperCAmelCase											=     1
									_UpperCAmelCase											=     FlaxViTForImageClassification(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE		)






		def       UpperCAmelCase__							(     self		)					->			str:




									"""simple docstring"""





									_UpperCAmelCase											=     self.prepare_config_and_inputs()
									(
									    (
									    _UpperCAmelCase
									)						,			(
									    _UpperCAmelCase
									)						,			
									)											=     config_and_inputs
									_UpperCAmelCase											=     {'pixel_values': pixel_values}
									return config, inputs_dict



@require_flax
class 						__a (							UpperCAmelCase       , unittest.TestCase     ):
		_a      :    List[str]           =      (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
		def       UpperCAmelCase__							(     self		)					->			None:




									"""simple docstring"""





									_UpperCAmelCase											=     FlaxViTModelTester(self		)
									_UpperCAmelCase											=     ConfigTester(self     ,       config_class=_SCREAMING_SNAKE_CASE     ,       has_text_modality=_SCREAMING_SNAKE_CASE     ,       hidden_size=37		)
		def       UpperCAmelCase__							(     self		)					->			Dict:




									"""simple docstring"""





									self.config_tester.run_common_tests()
		def       UpperCAmelCase__							(     self		)					->			Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			str:




									"""simple docstring"""





									_UpperCAmelCase						,			_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																_UpperCAmelCase											=     model_class(_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase											=     inspect.signature(model.__call__		)
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																_UpperCAmelCase											=     [*signature.parameters.keys()]

																_UpperCAmelCase											=     ['pixel_values']
																self.assertListEqual(arg_names[:1]     ,       _SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase						,			_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__		):
																							_UpperCAmelCase											=     self._prepare_for_class(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
																							_UpperCAmelCase											=     model_class(_SCREAMING_SNAKE_CASE		)

																							@jax.jit
																							def model_jitted(_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		):
																														return model(pixel_values=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)

																							with self.subTest('JIT Enabled'		):
																														_UpperCAmelCase											=     model_jitted(**_SCREAMING_SNAKE_CASE		).to_tuple()

																							with self.subTest('JIT Disabled'		):
																														with jax.disable_jit():
																																					_UpperCAmelCase											=     model_jitted(**_SCREAMING_SNAKE_CASE		).to_tuple()

																							self.assertEqual(len(_SCREAMING_SNAKE_CASE		)     ,       len(_SCREAMING_SNAKE_CASE		)		)
																							for jitted_output, output in zip(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		):
																														self.assertEqual(jitted_output.shape     ,       output.shape		)






		@slow
		def       UpperCAmelCase__							(     self		)					->			Dict:




									"""simple docstring"""





									for model_class_name in self.all_model_classes:
																_UpperCAmelCase											=     model_class_name.from_pretrained('google/vit-base-patch16-224'		)
																_UpperCAmelCase											=     model(np.ones((1, 3, 224, 224)		)		)
																self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
