



"""simple docstring"""

import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
			from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor







class    __lowercase     :







						"""simple docstring"""







						def __init__(self   ,		lowercase__   ,		lowercase__=13   ,		lowercase__=32   ,		lowercase__=2   ,		lowercase__=3   ,		lowercase__=16   ,		lowercase__=[1, 2, 1]   ,		lowercase__=[2, 2, 4]   ,		lowercase__=2   ,		lowercase__=2.0   ,		lowercase__=True   ,		lowercase__=0.0   ,		lowercase__=0.0   ,		lowercase__=0.1   ,		lowercase__="gelu"   ,		lowercase__=False   ,		lowercase__=True   ,		lowercase__=0.02   ,		lowercase__=1e-5   ,		lowercase__=True   ,		lowercase__=None   ,		lowercase__=True   ,		lowercase__=10   ,		lowercase__=8   ,		):
											snake_case_					:		int 					=					parent
											snake_case_					:		Union[str, Any] 					=					batch_size
											snake_case_					:		List[Any] 					=					image_size
											snake_case_					:		int 					=					patch_size
											snake_case_					:		List[str] 					=					num_channels
											snake_case_					:		Dict 					=					embed_dim
											snake_case_					:		Union[str, Any] 					=					depths
											snake_case_					:		Any 					=					num_heads
											snake_case_					:		Optional[int] 					=					window_size
											snake_case_					:		Union[str, Any] 					=					mlp_ratio
											snake_case_					:		Any 					=					qkv_bias
											snake_case_					:		Tuple 					=					hidden_dropout_prob
											snake_case_					:		Optional[Any] 					=					attention_probs_dropout_prob
											snake_case_					:		Optional[int] 					=					drop_path_rate
											snake_case_					:		Tuple 					=					hidden_act
											snake_case_					:		Optional[Any] 					=					use_absolute_embeddings
											snake_case_					:		List[Any] 					=					patch_norm
											snake_case_					:		Optional[int] 					=					layer_norm_eps
											snake_case_					:		Optional[Any] 					=					initializer_range
											snake_case_					:		str 					=					is_training
											snake_case_					:		Optional[int] 					=					scope
											snake_case_					:		Union[str, Any] 					=					use_labels
											snake_case_					:		int 					=					type_sequence_label_size
											snake_case_					:		str 					=					encoder_stride







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Dict 					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

											snake_case_					:		Tuple 					=					None
											if self.use_labels:
																snake_case_					:		str 					=					ids_tensor([self.batch_size]   ,		self.type_sequence_label_size       )

											snake_case_					:		int 					=					self.get_config()

											return config, pixel_values, labels







						def 						__UpperCamelCase							(self       ):
											return SwinvaConfig(
											    image_size=self.image_size   ,		patch_size=self.patch_size   ,		num_channels=self.num_channels   ,		embed_dim=self.embed_dim   ,		depths=self.depths   ,		num_heads=self.num_heads   ,		window_size=self.window_size   ,		mlp_ratio=self.mlp_ratio   ,		qkv_bias=self.qkv_bias   ,		hidden_dropout_prob=self.hidden_dropout_prob   ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,		drop_path_rate=self.drop_path_rate   ,		hidden_act=self.hidden_act   ,		use_absolute_embeddings=self.use_absolute_embeddings   ,		path_norm=self.patch_norm   ,		layer_norm_eps=self.layer_norm_eps   ,		initializer_range=self.initializer_range   ,		encoder_stride=self.encoder_stride   ,		)







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		Tuple 					=					SwinvaModel(config=lowercase__       )
											model.to(lowercase__       )
											model.eval()
											snake_case_					:		Union[str, Any] 					=					model(lowercase__       )

											snake_case_					:		Dict 					=					((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths       ) - 1))
											snake_case_					:		Dict 					=					int(config.embed_dim * 2 ** (len(config.depths       ) - 1)       )

											self.parent.assertEqual(result.last_hidden_state.shape   ,		(self.batch_size, expected_seq_len, expected_dim)       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		Optional[Any] 					=					SwinvaForMaskedImageModeling(config=lowercase__       )
											model.to(lowercase__       )
											model.eval()
											snake_case_					:		str 					=					model(lowercase__       )
											self.parent.assertEqual(
											    result.logits.shape   ,		(self.batch_size, self.num_channels, self.image_size, self.image_size)       )

											# test greyscale images
											snake_case_					:		str 					=					1
											snake_case_					:		List[str] 					=					SwinvaForMaskedImageModeling(lowercase__       )
											model.to(lowercase__       )
											model.eval()

											snake_case_					:		List[Any] 					=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
											snake_case_					:		Optional[Any] 					=					model(lowercase__       )
											self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, 1, self.image_size, self.image_size)       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		List[Any] 					=					self.type_sequence_label_size
											snake_case_					:		Optional[Any] 					=					SwinvaForImageClassification(lowercase__       )
											model.to(lowercase__       )
											model.eval()
											snake_case_					:		Any 					=					model(lowercase__   ,		labels=lowercase__       )
											self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.type_sequence_label_size)       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		int 					=					self.prepare_config_and_inputs()
											snake_case_					,			snake_case_					,			snake_case_					:		Dict 					=					config_and_inputs
											snake_case_					:		Dict 					=					{"""pixel_values""": pixel_values}
											return config, inputs_dict







@require_torch
class    __lowercase     (					_UpperCAmelCase					,    _UpperCAmelCase					,    unittest.TestCase):







						"""simple docstring"""







						_A		:							str							       =		(
						    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
						)
						_A		:							Optional[Any]							       =		(
						    {"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
						    if is_torch_available()
						    else {}
						)

						_A		:							List[Any]							       =		False
						_A		:							Union[str, Any]							       =		False
						_A		:							Optional[Any]							       =		False
						_A		:							Tuple							       =		False







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		int 					=					SwinvaModelTester(self       )
											snake_case_					:		Union[str, Any] 					=					ConfigTester(self   ,		config_class=lowercase__   ,		embed_dim=37       )







						def 						__UpperCamelCase							(self       ):
											self.config_tester.create_and_test_config_to_json_string()
											self.config_tester.create_and_test_config_to_json_file()
											self.config_tester.create_and_test_config_from_and_save_pretrained()
											self.config_tester.create_and_test_config_with_num_labels()
											self.config_tester.check_config_can_be_init_without_params()
											self.config_tester.check_config_arguments_init()







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[int] 					=					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*lowercase__       )







						@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip(reason="""Swinv2 does not use inputs_embeds"""       )
						def 						__UpperCamelCase							(self       ):
											pass







						def 						__UpperCamelCase							(self       ):
											snake_case_					,			snake_case_					:		Any 					=					self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																snake_case_					:		List[str] 					=					model_class(lowercase__       )
																self.assertIsInstance(model.get_input_embeddings()   ,		(nn.Module)       )
																snake_case_					:		Any 					=					model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(lowercase__   ,		nn.Linear       )       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					,			snake_case_					:		Any 					=					self.model_tester.prepare_config_and_inputs_for_common()

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

																snake_case_					:		Dict 					=					["""pixel_values"""]
																self.assertListEqual(arg_names[:1]   ,		lowercase__       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					,			snake_case_					:		Tuple 					=					self.model_tester.prepare_config_and_inputs_for_common()
											snake_case_					:		Any 					=					True

											for model_class in self.all_model_classes:
																snake_case_					:		Union[str, Any] 					=					True
																snake_case_					:		str 					=					False
																snake_case_					:		Any 					=					True
																snake_case_					:		Tuple 					=					model_class(lowercase__       )
																model.to(lowercase__       )
																model.eval()
																with torch.no_grad():
																					snake_case_					:		Optional[int] 					=					model(**self._prepare_for_class(lowercase__   ,		lowercase__       )       )
																snake_case_					:		List[str] 					=					outputs.attentions
																snake_case_					:		List[str] 					=					len(self.model_tester.depths       )
																self.assertEqual(len(lowercase__       )   ,		lowercase__       )

																# check that output_attentions also work using config
																del inputs_dict["output_attentions"]
																snake_case_					:		List[Any] 					=					True
																snake_case_					:		str 					=					config.window_size**2
																snake_case_					:		Any 					=					model_class(lowercase__       )
																model.to(lowercase__       )
																model.eval()
																with torch.no_grad():
																					snake_case_					:		Any 					=					model(**self._prepare_for_class(lowercase__   ,		lowercase__       )       )
																snake_case_					:		Optional[int] 					=					outputs.attentions
																self.assertEqual(len(lowercase__       )   ,		lowercase__       )

																self.assertListEqual(
																    list(attentions[0].shape[-3:]       )   ,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]   ,		)
																snake_case_					:		int 					=					len(lowercase__       )

																# Check attention is always last and order is fine
																snake_case_					:		Tuple 					=					True
																snake_case_					:		Any 					=					True
																snake_case_					:		List[Any] 					=					model_class(lowercase__       )
																model.to(lowercase__       )
																model.eval()
																with torch.no_grad():
																					snake_case_					:		str 					=					model(**self._prepare_for_class(lowercase__   ,		lowercase__       )       )

																if hasattr(self.model_tester   ,		"""num_hidden_states_types"""       ):
																					snake_case_					:		str 					=					self.model_tester.num_hidden_states_types
																else:
																					# also another +1 for reshaped_hidden_states
																					snake_case_					:		List[Any] 					=					2
																self.assertEqual(out_len + added_hidden_states   ,		len(lowercase__       )       )

																snake_case_					:		Any 					=					outputs.attentions

																self.assertEqual(len(lowercase__       )   ,		lowercase__       )

																self.assertListEqual(
																    list(self_attentions[0].shape[-3:]       )   ,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]   ,		)







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		Any 					=					model_class(lowercase__       )
											model.to(lowercase__       )
											model.eval()

											with torch.no_grad():
																snake_case_					:		Union[str, Any] 					=					model(**self._prepare_for_class(lowercase__   ,		lowercase__       )       )

											snake_case_					:		List[str] 					=					outputs.hidden_states

											snake_case_					:		List[Any] 					=					getattr(
											    self.model_tester   ,		"""expected_num_hidden_layers"""   ,		len(self.model_tester.depths       ) + 1       )
											self.assertEqual(len(lowercase__       )   ,		lowercase__       )

											# Swinv2 has a different seq_length
											snake_case_					:		List[Any] 					=					(
											    config.patch_size
											    if isinstance(config.patch_size   ,		collections.abc.Iterable       )
											    else (config.patch_size, config.patch_size)
											)

											snake_case_					:		Tuple 					=					(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]       )   ,		[num_patches, self.model_tester.embed_dim]   ,		)

											snake_case_					:		Union[str, Any] 					=					outputs.reshaped_hidden_states
											self.assertEqual(len(lowercase__       )   ,		lowercase__       )

											snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					:		Union[str, Any] 					=					reshaped_hidden_states[0].shape
											snake_case_					:		List[str] 					=					(
											    reshaped_hidden_states[0].view(lowercase__   ,		lowercase__   ,		height * width       ).permute(0   ,		2   ,		1       )
											)
											self.assertListEqual(
											    list(reshaped_hidden_states.shape[-2:]       )   ,		[num_patches, self.model_tester.embed_dim]   ,		)







						def 						__UpperCamelCase							(self       ):
											snake_case_					,			snake_case_					:		Any 					=					self.model_tester.prepare_config_and_inputs_for_common()

											snake_case_					:		Dict 					=					(
											    self.model_tester.image_size
											    if isinstance(self.model_tester.image_size   ,		collections.abc.Iterable       )
											    else (self.model_tester.image_size, self.model_tester.image_size)
											)

											for model_class in self.all_model_classes:
																snake_case_					:		Optional[int] 					=					True
																self.check_hidden_states_output(lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__       )

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																snake_case_					:		str 					=					True

																self.check_hidden_states_output(lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					,			snake_case_					:		str 					=					self.model_tester.prepare_config_and_inputs_for_common()
											snake_case_					:		Optional[Any] 					=					3

											snake_case_					:		Optional[int] 					=					(
											    self.model_tester.image_size
											    if isinstance(self.model_tester.image_size   ,		collections.abc.Iterable       )
											    else (self.model_tester.image_size, self.model_tester.image_size)
											)
											snake_case_					:		List[Any] 					=					(
											    config.patch_size
											    if isinstance(config.patch_size   ,		collections.abc.Iterable       )
											    else (config.patch_size, config.patch_size)
											)

											snake_case_					:		List[Any] 					=					image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
											snake_case_					:		int 					=					image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

											for model_class in self.all_model_classes:
																snake_case_					:		Optional[int] 					=					True
																self.check_hidden_states_output(lowercase__   ,		lowercase__   ,		lowercase__   ,		(padded_height, padded_width)       )

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																snake_case_					:		List[str] 					=					True
																self.check_hidden_states_output(lowercase__   ,		lowercase__   ,		lowercase__   ,		(padded_height, padded_width)       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Any 					=					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*lowercase__       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		List[str] 					=					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*lowercase__       )







						@slow
						def 						__UpperCamelCase							(self       ):
											for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																snake_case_					:		Dict 					=					SwinvaModel.from_pretrained(lowercase__       )
																self.assertIsNotNone(lowercase__       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					,			snake_case_					:		Optional[Any] 					=					self.model_tester.prepare_config_and_inputs_for_common()

											snake_case_					:		Optional[Any] 					=					_config_zero_init(lowercase__       )
											for model_class in self.all_model_classes:
																snake_case_					:		Union[str, Any] 					=					model_class(config=lowercase__       )
																for name, param in model.named_parameters():
																					if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
																										self.assertIn(
																										    ((param.data.mean() * 1e9).round() / 1e9).item()   ,		[0.0, 1.0]   ,		msg=f'Parameter {name} of model {model_class} seems not properly initialized'   ,		)







@require_vision
@require_torch
class    __lowercase     (					unittest.TestCase):







						"""simple docstring"""







						@cached_property
						def 						__UpperCamelCase							(self       ):
											return (
											    AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""       )
											    if is_vision_available()
											    else None
											)







						@slow
						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[int] 					=					SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""       ).to(
											    lowercase__       )
											snake_case_					:		Any 					=					self.default_image_processor

											snake_case_					:		Union[str, Any] 					=					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
											snake_case_					:		int 					=					image_processor(images=lowercase__   ,		return_tensors="""pt"""       ).to(lowercase__       )

											# forward pass
											with torch.no_grad():
																snake_case_					:		Optional[int] 					=					model(**lowercase__       )

											# verify the logits
											snake_case_					:		Any 					=					torch.Size((1, 10_00)       )
											self.assertEqual(outputs.logits.shape   ,		lowercase__       )
											snake_case_					:		str 					=					torch.tensor([-0.3947, -0.4306, 0.0026]       ).to(lowercase__       )
											self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,		lowercase__   ,		atol=1e-4       )       )









"""simple docstring"""

import argparse
import copy







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Any			):



					"""simple docstring"""





					snake_case_					:		List[Any] 					=					{}

					with open(SCREAMING_SNAKE_CASE__			) as f:
										for line in f:
															if line.split()[0] not in dict_of_neighbours:
																				snake_case_					:		int 					=					[]
																				_list.append([line.split()[1], line.split()[2]]			)
																				snake_case_					:		Optional[Any] 					=					_list
															else:
																				dict_of_neighbours[line.split()[0]].append(
																				    [line.split()[1], line.split()[2]]			)
															if line.split()[1] not in dict_of_neighbours:
																				snake_case_					:		str 					=					[]
																				_list.append([line.split()[0], line.split()[2]]			)
																				snake_case_					:		Optional[Any] 					=					_list
															else:
																				dict_of_neighbours[line.split()[1]].append(
																				    [line.split()[0], line.split()[2]]			)

					return dict_of_neighbours







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  str			):



					"""simple docstring"""





					with open(SCREAMING_SNAKE_CASE__			) as f:
										snake_case_					:		Optional[Any] 					=					f.read(1			)
					snake_case_					:		Union[str, Any] 					=					start_node

					snake_case_					:		Dict 					=					[]

					snake_case_					:		Union[str, Any] 					=					start_node

					snake_case_					:		Tuple 					=					0
					while visiting not in first_solution:
										snake_case_					:		int 					=					1_0_0_0_0
										for k in dict_of_neighbours[visiting]:
															if int(k[1]			) < int(SCREAMING_SNAKE_CASE__			) and k[0] not in first_solution:
																				snake_case_					:		Union[str, Any] 					=					k[1]
																				snake_case_					:		Any 					=					k[0]

										first_solution.append(SCREAMING_SNAKE_CASE__			)
										snake_case_					:		Tuple 					=					distance_of_first_solution + int(SCREAMING_SNAKE_CASE__			)
										snake_case_					:		List[str] 					=					best_node

					first_solution.append(SCREAMING_SNAKE_CASE__			)

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

					snake_case_					:		int 					=					(
					    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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Union[str, Any]				,    SCREAMING_SNAKE_CASE__ :  Optional[int]			):



					"""simple docstring"""





					snake_case_					:		Union[str, Any] 					=					[]

					for n in solution[1:-1]:
										snake_case_					:		str 					=					solution.index(SCREAMING_SNAKE_CASE__			)
										for kn in solution[1:-1]:
															snake_case_					:		Tuple 					=					solution.index(SCREAMING_SNAKE_CASE__			)
															if n == kn:
																				continue

															snake_case_					:		Optional[Any] 					=					copy.deepcopy(SCREAMING_SNAKE_CASE__			)
															snake_case_					:		int 					=					kn
															snake_case_					:		Dict 					=					n

															snake_case_					:		Optional[int] 					=					0

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

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

					snake_case_					:		Optional[Any] 					=					len(neighborhood_of_solution[0]			) - 1

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







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  List[Any]			):



					"""simple docstring"""





					snake_case_					:		Dict 					=					1
					snake_case_					:		List[Any] 					=					first_solution
					snake_case_					:		List[Any] 					=					[]
					snake_case_					:		Optional[Any] 					=					distance_of_first_solution
					snake_case_					:		Dict 					=					solution

					while count <= iters:
										snake_case_					:		List[str] 					=					find_neighborhood(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
										snake_case_					:		List[Any] 					=					0
										snake_case_					:		List[Any] 					=					neighborhood[index_of_best_solution]
										snake_case_					:		Union[str, Any] 					=					len(SCREAMING_SNAKE_CASE__			) - 1

										snake_case_					:		List[str] 					=					False
										while not found:
															snake_case_					:		Tuple 					=					0
															while i < len(SCREAMING_SNAKE_CASE__			):
																				if best_solution[i] != solution[i]:
																									snake_case_					:		Optional[Any] 					=					best_solution[i]
																									snake_case_					:		int 					=					solution[i]
																									break
																				snake_case_					:		List[str] 					=					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]			)
																				snake_case_					:		Tuple 					=					True
																				snake_case_					:		Dict 					=					best_solution[:-1]
																				snake_case_					:		Tuple 					=					neighborhood[index_of_best_solution][best_cost_index]
																				if cost < best_cost:
																									snake_case_					:		Tuple 					=					cost
																									snake_case_					:		Union[str, Any] 					=					solution
															else:
																				snake_case_					:		str 					=					index_of_best_solution + 1
																				snake_case_					:		Tuple 					=					neighborhood[index_of_best_solution]

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

										snake_case_					:		List[str] 					=					count + 1

					return best_solution_ever, best_cost







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[Any]=None			):



					"""simple docstring"""





					snake_case_					:		Tuple 					=					generate_neighbours(args.File			)

					snake_case_					,			snake_case_					:		Optional[Any] 					=					generate_first_solution(
					    args.File				,    SCREAMING_SNAKE_CASE__			)

					snake_case_					,			snake_case_					:		Dict 					=					tabu_search(
					    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    args.Iterations				,    args.Size				,    )

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


if __name__ == "__main__":
			a_			   =					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())




