

'''simple docstring'''





import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
							    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
							    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
							    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
							    LayoutLMvaConfig,
							    LayoutLMvaForQuestionAnswering,
							    LayoutLMvaForSequenceClassification,
							    LayoutLMvaForTokenClassification,
							    LayoutLMvaModel,
							)
							from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
							from PIL import Image

							from transformers import LayoutLMvaImageProcessor





class 		__SCREAMING_SNAKE_CASE       :







				def __init__(					self    :		Optional[Any]		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		int=2		,				UpperCAmelCase__    :		Optional[int]=3		,				UpperCAmelCase__    :		Any=4		,				UpperCAmelCase__    :		Optional[Any]=2		,				UpperCAmelCase__    :		str=7		,				UpperCAmelCase__    :		List[str]=True		,				UpperCAmelCase__    :		Optional[Any]=True		,				UpperCAmelCase__    :		Optional[Any]=True		,				UpperCAmelCase__    :		List[str]=True		,				UpperCAmelCase__    :		List[str]=99		,				UpperCAmelCase__    :		Tuple=36		,				UpperCAmelCase__    :		Union[str, Any]=3		,				UpperCAmelCase__    :		Optional[int]=4		,				UpperCAmelCase__    :		List[Any]=37		,				UpperCAmelCase__    :		List[str]="gelu"		,				UpperCAmelCase__    :		Dict=0.1		,				UpperCAmelCase__    :		List[str]=0.1		,				UpperCAmelCase__    :		Optional[int]=512		,				UpperCAmelCase__    :		Optional[int]=16		,				UpperCAmelCase__    :		Optional[Any]=2		,				UpperCAmelCase__    :		int=0.02		,				UpperCAmelCase__    :		Optional[Any]=6		,				UpperCAmelCase__    :		Optional[int]=6		,				UpperCAmelCase__    :		Any=3		,				UpperCAmelCase__    :		Union[str, Any]=4		,				UpperCAmelCase__    :		Union[str, Any]=None		,				UpperCAmelCase__    :		Dict=1000		,				):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=parent
										lowercase   :       Tuple								=batch_size
										lowercase   :       Union[str, Any]								=num_channels
										lowercase   :       str								=image_size
										lowercase   :       str								=patch_size
										lowercase   :       Any								=text_seq_length
										lowercase   :       List[Any]								=is_training
										lowercase   :       Optional[Any]								=use_input_mask
										lowercase   :       Union[str, Any]								=use_token_type_ids
										lowercase   :       int								=use_labels
										lowercase   :       List[str]								=vocab_size
										lowercase   :       Tuple								=hidden_size
										lowercase   :       Tuple								=num_hidden_layers
										lowercase   :       Tuple								=num_attention_heads
										lowercase   :       Dict								=intermediate_size
										lowercase   :       int								=hidden_act
										lowercase   :       int								=hidden_dropout_prob
										lowercase   :       str								=attention_probs_dropout_prob
										lowercase   :       Any								=max_position_embeddings
										lowercase   :       Any								=type_vocab_size
										lowercase   :       Union[str, Any]								=type_sequence_label_size
										lowercase   :       Tuple								=initializer_range
										lowercase   :       int								=coordinate_size
										lowercase   :       List[Any]								=shape_size
										lowercase   :       str								=num_labels
										lowercase   :       Dict								=num_choices
										lowercase   :       Optional[int]								=scope
										lowercase   :       Dict								=range_bbox

										# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
										lowercase   :       Any								=text_seq_length
										lowercase   :       List[Any]								=(image_size // patch_size) ** 2 + 1
										lowercase   :       Optional[int]								=self.text_seq_length + self.image_seq_length







				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=ids_tensor([self.batch_size, self.text_seq_length]		,				self.vocab_size      )

										lowercase   :       int								=ids_tensor([self.batch_size, self.text_seq_length, 4]		,				self.range_bbox      )
										# Ensure that bbox is legal
										for i in range(bbox.shape[0]      ):
																for j in range(bbox.shape[1]      ):
																						if bbox[i, j, 3] < bbox[i, j, 1]:
																												lowercase   :       Dict								=bbox[i, j, 3]
																												lowercase   :       int								=bbox[i, j, 1]
																												lowercase   :       Dict								=t
																						if bbox[i, j, 2] < bbox[i, j, 0]:
																												lowercase   :       Optional[int]								=bbox[i, j, 2]
																												lowercase   :       str								=bbox[i, j, 0]
																												lowercase   :       Union[str, Any]								=t

										lowercase   :       Dict								=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

										lowercase   :       Optional[int]								=None
										if self.use_input_mask:
																lowercase   :       Optional[int]								=random_attention_mask([self.batch_size, self.text_seq_length]      )

										lowercase   :       Dict								=None
										if self.use_token_type_ids:
																lowercase   :       Dict								=ids_tensor([self.batch_size, self.text_seq_length]		,				self.type_vocab_size      )

										lowercase   :       Union[str, Any]								=None
										lowercase   :       Union[str, Any]								=None
										if self.use_labels:
																lowercase   :       Tuple								=ids_tensor([self.batch_size]		,				self.type_sequence_label_size      )
																lowercase   :       Optional[Any]								=ids_tensor([self.batch_size, self.text_seq_length]		,				self.num_labels      )

										lowercase   :       Union[str, Any]								=LayoutLMvaConfig(
										    vocab_size=self.vocab_size		,				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		,				max_position_embeddings=self.max_position_embeddings		,				type_vocab_size=self.type_vocab_size		,				initializer_range=self.initializer_range		,				coordinate_size=self.coordinate_size		,				shape_size=self.shape_size		,				input_size=self.image_size		,				patch_size=self.patch_size		,				)

										return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels







				def    lowerCamelCase_  (					self    :		Any		,				UpperCAmelCase__    :		Tuple		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		List[str]		,				UpperCAmelCase__    :		Any		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		List[str]		,				UpperCAmelCase__    :		Any		,				UpperCAmelCase__    :		Any      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								=LayoutLMvaModel(config=UpperCAmelCase__      )
										model.to(UpperCAmelCase__      )
										model.eval()

										# text + image
										lowercase   :       Union[str, Any]								=model(UpperCAmelCase__		,				pixel_values=UpperCAmelCase__      )
										lowercase   :       int								=model(
										    UpperCAmelCase__		,				bbox=UpperCAmelCase__		,				pixel_values=UpperCAmelCase__		,				attention_mask=UpperCAmelCase__		,				token_type_ids=UpperCAmelCase__      )
										lowercase   :       Dict								=model(UpperCAmelCase__		,				bbox=UpperCAmelCase__		,				pixel_values=UpperCAmelCase__		,				token_type_ids=UpperCAmelCase__      )
										lowercase   :       List[Any]								=model(UpperCAmelCase__		,				bbox=UpperCAmelCase__		,				pixel_values=UpperCAmelCase__      )

										self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)      )

										# text only
										lowercase   :       List[Any]								=model(UpperCAmelCase__      )
										self.parent.assertEqual(
										    result.last_hidden_state.shape		,				(self.batch_size, self.text_seq_length, self.hidden_size)      )

										# image only
										lowercase   :       Optional[Any]								=model(pixel_values=UpperCAmelCase__      )
										self.parent.assertEqual(
										    result.last_hidden_state.shape		,				(self.batch_size, self.image_seq_length, self.hidden_size)      )







				def    lowerCamelCase_  (					self    :		List[str]		,				UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		Dict		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		Union[str, Any]		,				UpperCAmelCase__    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								=self.num_labels
										lowercase   :       Tuple								=LayoutLMvaForSequenceClassification(UpperCAmelCase__      )
										model.to(UpperCAmelCase__      )
										model.eval()
										lowercase   :       Union[str, Any]								=model(
										    UpperCAmelCase__		,				bbox=UpperCAmelCase__		,				pixel_values=UpperCAmelCase__		,				attention_mask=UpperCAmelCase__		,				token_type_ids=UpperCAmelCase__		,				labels=UpperCAmelCase__		,				)
										self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels)      )







				def    lowerCamelCase_  (					self    :		Tuple		,				UpperCAmelCase__    :		Union[str, Any]		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		Union[str, Any]		,				UpperCAmelCase__    :		Tuple		,				UpperCAmelCase__    :		List[str]		,				UpperCAmelCase__    :		List[str]		,				UpperCAmelCase__    :		Union[str, Any]		,				UpperCAmelCase__    :		Dict      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								=self.num_labels
										lowercase   :       Dict								=LayoutLMvaForTokenClassification(config=UpperCAmelCase__      )
										model.to(UpperCAmelCase__      )
										model.eval()
										lowercase   :       Tuple								=model(
										    UpperCAmelCase__		,				bbox=UpperCAmelCase__		,				pixel_values=UpperCAmelCase__		,				attention_mask=UpperCAmelCase__		,				token_type_ids=UpperCAmelCase__		,				labels=UpperCAmelCase__		,				)
										self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.text_seq_length, self.num_labels)      )







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		List[str]		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		Tuple		,				UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		Optional[Any]		,				UpperCAmelCase__    :		Any		,				UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=LayoutLMvaForQuestionAnswering(config=UpperCAmelCase__      )
										model.to(UpperCAmelCase__      )
										model.eval()
										lowercase   :       List[str]								=model(
										    UpperCAmelCase__		,				bbox=UpperCAmelCase__		,				pixel_values=UpperCAmelCase__		,				attention_mask=UpperCAmelCase__		,				token_type_ids=UpperCAmelCase__		,				start_positions=UpperCAmelCase__		,				end_positions=UpperCAmelCase__		,				)
										self.parent.assertEqual(result.start_logits.shape		,				(self.batch_size, self.seq_length)      )
										self.parent.assertEqual(result.end_logits.shape		,				(self.batch_size, self.seq_length)      )







				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										lowercase   :       List[str]								=self.prepare_config_and_inputs()
										(
										    lowercase  
										)   :       Optional[Any]								=config_and_inputs
										lowercase   :       str								={
										    '''input_ids''': input_ids,
										    '''bbox''': bbox,
										    '''pixel_values''': pixel_values,
										    '''token_type_ids''': token_type_ids,
										    '''attention_mask''': input_mask,
										}
										return config, inputs_dict





@require_torch
class 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						False
				lowerCamelCase_							=						False
				lowerCamelCase_							=						False

				lowerCamelCase_							=						(
				    (
				        LayoutLMvaModel,
				        LayoutLMvaForSequenceClassification,
				        LayoutLMvaForTokenClassification,
				        LayoutLMvaForQuestionAnswering,
				    )
				    if is_torch_available()
				    else ()
				)
				lowerCamelCase_							=						(
				    {'document-question-answering': LayoutLMvaForQuestionAnswering, 'feature-extraction': LayoutLMvaModel}
				    if is_torch_available()
				    else {}
				)







				def    lowerCamelCase_  (					self    :		str		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		List[str]		,				UpperCAmelCase__    :		Tuple      ):
										'''simple docstring'''
										return True







				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=LayoutLMvaModelTester(self      )
										lowercase   :       List[str]								=ConfigTester(self		,				config_class=UpperCAmelCase__		,				hidden_size=37      )







				def    lowerCamelCase_  (					self    :		str		,				UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		Dict=False      ):
										'''simple docstring'''
										lowercase   :       List[str]								=copy.deepcopy(UpperCAmelCase__      )
										if model_class in get_values(UpperCAmelCase__      ):
																lowercase   :       List[Any]								={
																    k: v.unsqueeze(1      ).expand(-1		,				self.model_tester.num_choices		,				-1      ).contiguous()
																    if isinstance(UpperCAmelCase__		,				torch.Tensor      ) and v.ndim > 1
																    else v
																    for k, v in inputs_dict.items()
																}
										if return_labels:
																if model_class in get_values(UpperCAmelCase__      ):
																						lowercase   :       List[str]								=torch.ones(self.model_tester.batch_size		,				dtype=torch.long		,				device=UpperCAmelCase__      )
																elif model_class in get_values(UpperCAmelCase__      ):
																						lowercase   :       Tuple								=torch.zeros(
																						    self.model_tester.batch_size		,				dtype=torch.long		,				device=UpperCAmelCase__      )
																						lowercase   :       Optional[int]								=torch.zeros(
																						    self.model_tester.batch_size		,				dtype=torch.long		,				device=UpperCAmelCase__      )
																elif model_class in [
																    *get_values(UpperCAmelCase__      ),
																]:
																						lowercase   :       Any								=torch.zeros(
																						    self.model_tester.batch_size		,				dtype=torch.long		,				device=UpperCAmelCase__      )
																elif model_class in [
																    *get_values(UpperCAmelCase__      ),
																]:
																						lowercase   :       Any								=torch.zeros(
																						    (self.model_tester.batch_size, self.model_tester.text_seq_length)		,				dtype=torch.long		,				device=UpperCAmelCase__		,				)

										return inputs_dict







				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										self.config_tester.run_common_tests()







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																lowercase   :       Optional[Any]								=type
																self.model_tester.create_and_check_model(*UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__      )







				@slow
				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																lowercase   :       List[str]								=LayoutLMvaModel.from_pretrained(UpperCAmelCase__      )
																self.assertIsNotNone(UpperCAmelCase__      )






def 							_lowerCAmelCase (      )							->     List[str]:
						lowercase   :       Dict								=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
						return image





@require_torch
class 		__SCREAMING_SNAKE_CASE       ( unittest.TestCase			):







				@cached_property
				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										return LayoutLMvaImageProcessor(apply_ocr=UpperCAmelCase__      ) if is_vision_available() else None







				@slow
				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''      ).to(UpperCAmelCase__      )

										lowercase   :       Optional[Any]								=self.default_image_processor
										lowercase   :       Union[str, Any]								=prepare_img()
										lowercase   :       Union[str, Any]								=image_processor(images=UpperCAmelCase__		,				return_tensors='''pt'''      ).pixel_values.to(UpperCAmelCase__      )

										lowercase   :       Any								=torch.tensor([[1, 2]]      )
										lowercase   :       Union[str, Any]								=torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]      ).unsqueeze(0      )

										# forward pass
										lowercase   :       int								=model(
										    input_ids=input_ids.to(UpperCAmelCase__      )		,				bbox=bbox.to(UpperCAmelCase__      )		,				pixel_values=pixel_values.to(UpperCAmelCase__      )		,				)

										# verify the logits
										lowercase   :       Union[str, Any]								=torch.Size((1, 199, 768)      )
										self.assertEqual(outputs.last_hidden_state.shape		,				UpperCAmelCase__      )

										lowercase   :       str								=torch.tensor(
										    [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]      ).to(UpperCAmelCase__      )

										self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]		,				UpperCAmelCase__		,				atol=1E-4      )      )








'''simple docstring'''





import argparse
import copy






def 							_lowerCAmelCase (      __magic_name__	:    List[str]	)							->     Union[str, Any]:
						lowercase   :       int								={}

						with open(__magic_name__	) as f:
												for line in f:
																		if line.split()[0] not in dict_of_neighbours:
																								lowercase   :       List[str]								=[]
																								_list.append([line.split()[1], line.split()[2]]	)
																								lowercase   :       Tuple								=_list
																		else:
																								dict_of_neighbours[line.split()[0]].append(
																								    [line.split()[1], line.split()[2]]	)
																		if line.split()[1] not in dict_of_neighbours:
																								lowercase   :       List[Any]								=[]
																								_list.append([line.split()[0], line.split()[2]]	)
																								lowercase   :       Union[str, Any]								=_list
																		else:
																								dict_of_neighbours[line.split()[1]].append(
																								    [line.split()[0], line.split()[2]]	)

						return dict_of_neighbours






def 							_lowerCAmelCase (      __magic_name__	:    Optional[int]      ,   __magic_name__	:    List[Any]	)							->     str:

						with open(__magic_name__	) as f:
												lowercase   :       Optional[int]								=f.read(1	)
						lowercase   :       List[Any]								=start_node

						lowercase   :       List[Any]								=[]

						lowercase   :       str								=start_node

						lowercase   :       str								=0
						while visiting not in first_solution:
												lowercase   :       Optional[int]								=10000
												for k in dict_of_neighbours[visiting]:
																		if int(k[1]	) < int(__magic_name__	) and k[0] not in first_solution:
																								lowercase   :       List[Any]								=k[1]
																								lowercase   :       str								=k[0]

												first_solution.append(__magic_name__	)
												lowercase   :       Any								=distance_of_first_solution + int(__magic_name__	)
												lowercase   :       Optional[int]								=best_node

						first_solution.append(__magic_name__	)

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

						lowercase   :       str								=(
						    distance_of_first_solution
						    + int(dict_of_neighbours[first_solution[-2]][position][1]	)
						    - 10000
						)
						return first_solution, distance_of_first_solution






def 							_lowerCAmelCase (      __magic_name__	:    str      ,   __magic_name__	:    Any	)							->     Tuple:
						lowercase   :       Tuple								=[]

						for n in solution[1:-1]:
												lowercase   :       Dict								=solution.index(__magic_name__	)
												for kn in solution[1:-1]:
																		lowercase   :       Tuple								=solution.index(__magic_name__	)
																		if n == kn:
																								continue

																		lowercase   :       Union[str, Any]								=copy.deepcopy(__magic_name__	)
																		lowercase   :       Optional[int]								=kn
																		lowercase   :       List[Any]								=n

																		lowercase   :       List[Any]								=0

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

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

						lowercase   :       Union[str, Any]								=len(neighborhood_of_solution[0]	) - 1

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






def 							_lowerCAmelCase (      __magic_name__	:    Any      ,   __magic_name__	:    str      ,   __magic_name__	:    List[Any]      ,   __magic_name__	:    Tuple      ,   __magic_name__	:    Dict	)							->     Union[str, Any]:
						lowercase   :       str								=1
						lowercase   :       List[Any]								=first_solution
						lowercase   :       Any								=[]
						lowercase   :       str								=distance_of_first_solution
						lowercase   :       str								=solution

						while count <= iters:
												lowercase   :       Union[str, Any]								=find_neighborhood(__magic_name__      ,   __magic_name__	)
												lowercase   :       Dict								=0
												lowercase   :       int								=neighborhood[index_of_best_solution]
												lowercase   :       Optional[int]								=len(__magic_name__	) - 1

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

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

												lowercase   :       Optional[int]								=count + 1

						return best_solution_ever, best_cost






def 							_lowerCAmelCase (      __magic_name__	:    str=None	)							->     Tuple:
						lowercase   :       List[str]								=generate_neighbours(args.File	)

						lowercase		,  lowercase   :       Optional[Any]								=generate_first_solution(
						    args.File      ,   __magic_name__	)

						lowercase		,  lowercase   :       int								=tabu_search(
						    __magic_name__      ,   __magic_name__      ,   __magic_name__      ,   args.Iterations      ,   args.Size      ,   )

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


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





