






import argparse
import copy







def      snake_case		(							UpperCAmelCase	:       List[str]   ):
				A	 =      {}

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

				return dict_of_neighbours







def      snake_case		(							UpperCAmelCase	:       Optional[Any],						UpperCAmelCase	:       Optional[int]   ):

				with open(UpperCAmelCase   ) as f:
								A	 =      f.read(1   )
				A	 =      start_node

				A	 =      []

				A	 =      start_node

				A	 =      0
				while visiting not in first_solution:
								A	 =      1_00_00
								for k in dict_of_neighbours[visiting]:
												if int(k[1]   ) < int(UpperCAmelCase   ) and k[0] not in first_solution:
																A	 =      k[1]
																A	 =      k[0]

								first_solution.append(UpperCAmelCase   )
								A	 =      distance_of_first_solution + int(UpperCAmelCase   )
								A	 =      best_node

				first_solution.append(UpperCAmelCase   )

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

				A	 =      (
				    distance_of_first_solution
				    + int(dict_of_neighbours[first_solution[-2]][position][1]   )
				    - 1_00_00
				)
				return first_solution, distance_of_first_solution







def      snake_case		(							UpperCAmelCase	:       Any,						UpperCAmelCase	:       Optional[int]   ):
				A	 =      []

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

												A	 =      copy.deepcopy(UpperCAmelCase   )
												A	 =      kn
												A	 =      n

												A	 =      0

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

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

				A	 =      len(neighborhood_of_solution[0]   ) - 1

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







def      snake_case		(							UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       Dict,						UpperCAmelCase	:       Optional[Any],						UpperCAmelCase	:       List[str],						UpperCAmelCase	:       Dict   ):
				A	 =      1
				A	 =      first_solution
				A	 =      []
				A	 =      distance_of_first_solution
				A	 =      solution

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

								A	 =      False
								while not found:
												A	 =      0
												while i < len(UpperCAmelCase   ):
																if best_solution[i] != solution[i]:
																				A	 =      best_solution[i]
																				A	 =      solution[i]
																				break
																A	 =      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]   )
																A	 =      True
																A	 =      best_solution[:-1]
																A	 =      neighborhood[index_of_best_solution][best_cost_index]
																if cost < best_cost:
																				A	 =      cost
																				A	 =      solution
												else:
																A	 =      index_of_best_solution + 1
																A	 =      neighborhood[index_of_best_solution]

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

								A	 =      count + 1

				return best_solution_ever, best_cost







def      snake_case		(							UpperCAmelCase	:       Optional[int]=None   ):
				A	 =      generate_neighbours(args.File   )

				A ,		A	 =      generate_first_solution(
				    args.File,						UpperCAmelCase   )

				A ,		A	 =      tabu_search(
				    UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase,						args.Iterations,						args.Size,						)

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


if __name__ == "__main__":
					lowerCAmelCase_           = 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 warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   ["image_processor", "tokenizer"]
		snake_case    =   "LayoutLMv2ImageProcessor"
		snake_case    =   ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")
		def __init__(  self		:       List[str]						,_SCREAMING_SNAKE_CASE		:       List[str]=None						,_SCREAMING_SNAKE_CASE		:       Optional[int]=None						,**_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   List[str]:


						'''simple docstring'''
						if "feature_extractor" in kwargs:
										warnings.warn(
										    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
										    ' instead.'						,_SCREAMING_SNAKE_CASE						,)
										A	 =      kwargs.pop('feature_extractor'		)

						A	 =      image_processor if image_processor is not None else feature_extractor
						if image_processor is None:
										raise ValueError('You need to specify an `image_processor`.'		)
						if tokenizer is None:
										raise ValueError('You need to specify a `tokenizer`.'		)

						super().__init__(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
		def __call__(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,_SCREAMING_SNAKE_CASE		:       Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None						,_SCREAMING_SNAKE_CASE		:       Union[List[List[int]], List[List[List[int]]]] = None						,_SCREAMING_SNAKE_CASE		:       Optional[Union[List[int], List[List[int]]]] = None						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       Union[bool, str, PaddingStrategy] = False						,_SCREAMING_SNAKE_CASE		:       Union[bool, str, TruncationStrategy] = None						,_SCREAMING_SNAKE_CASE		:       Optional[int] = None						,_SCREAMING_SNAKE_CASE		:       int = 0						,_SCREAMING_SNAKE_CASE		:       Optional[int] = None						,_SCREAMING_SNAKE_CASE		:       Optional[bool] = None						,_SCREAMING_SNAKE_CASE		:       Optional[bool] = None						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       Optional[Union[str, TensorType]] = None						,**_SCREAMING_SNAKE_CASE		:       Tuple						,)    ->   BatchEncoding:


						'''simple docstring'''
						# verify input
						if self.image_processor.apply_ocr and (boxes is not None):
										raise ValueError(
										    'You cannot provide bounding boxes '
										    'if you initialized the image processor with apply_ocr set to True.'		)

						if self.image_processor.apply_ocr and (word_labels is not None):
										raise ValueError(
										    'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'		)

						if return_overflowing_tokens is True and return_offsets_mapping is False:
										raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'		)

						# first, apply the image processor
						A	 =      self.image_processor(images=_SCREAMING_SNAKE_CASE						,return_tensors=_SCREAMING_SNAKE_CASE		)

						# second, apply the tokenizer
						if text is not None and self.image_processor.apply_ocr and text_pair is None:
										if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
														A	 =      [text]  # add batch dimension (as the image processor always adds a batch dimension)
										A	 =      features['words']

						A	 =      self.tokenizer(
						    text=text if text is not None else features['words']						,text_pair=text_pair if text_pair is not None else None						,boxes=boxes if boxes is not None else features['boxes']						,word_labels=_SCREAMING_SNAKE_CASE						,add_special_tokens=_SCREAMING_SNAKE_CASE						,padding=_SCREAMING_SNAKE_CASE						,truncation=_SCREAMING_SNAKE_CASE						,max_length=_SCREAMING_SNAKE_CASE						,stride=_SCREAMING_SNAKE_CASE						,pad_to_multiple_of=_SCREAMING_SNAKE_CASE						,return_token_type_ids=_SCREAMING_SNAKE_CASE						,return_attention_mask=_SCREAMING_SNAKE_CASE						,return_overflowing_tokens=_SCREAMING_SNAKE_CASE						,return_special_tokens_mask=_SCREAMING_SNAKE_CASE						,return_offsets_mapping=_SCREAMING_SNAKE_CASE						,return_length=_SCREAMING_SNAKE_CASE						,verbose=_SCREAMING_SNAKE_CASE						,return_tensors=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE						,)

						# add pixel values
						A	 =      features.pop('pixel_values'		)
						if return_overflowing_tokens is True:
										A	 =      self.get_overflowing_images(_SCREAMING_SNAKE_CASE						,encoded_inputs['overflow_to_sample_mapping']		)
						A	 =      images

						return encoded_inputs
		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       Optional[Any]		)    ->   Dict:


						'''simple docstring'''
						# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
						A	 =      []
						for sample_idx in overflow_to_sample_mapping:
										images_with_overflow.append(images[sample_idx]		)

						if len(_SCREAMING_SNAKE_CASE		) != len(_SCREAMING_SNAKE_CASE		):
										raise ValueError(
										    'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
										    f' {len(_SCREAMING_SNAKE_CASE		)} and {len(_SCREAMING_SNAKE_CASE		)}'		)

						return images_with_overflow
		def       A(  self		:       List[str]						,*_SCREAMING_SNAKE_CASE		:       str						,**_SCREAMING_SNAKE_CASE		:       Union[str, Any]		)    ->   Tuple:


						'''simple docstring'''
						return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Dict						,*_SCREAMING_SNAKE_CASE		:       Tuple						,**_SCREAMING_SNAKE_CASE		:       int		)    ->   int:


						'''simple docstring'''
						return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
		@property
		def       A(  self		:       Any		)    ->   Union[str, Any]:


						'''simple docstring'''
						return ["input_ids", "bbox", "attention_mask", "image"]
		@property
		def       A(  self		:       Dict		)    ->   int:


						'''simple docstring'''
						warnings.warn(
						    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'						,_SCREAMING_SNAKE_CASE						,)
						return self.image_processor_class







		@property
		def       A(  self		:       Optional[int]		)    ->   Union[str, Any]:


						'''simple docstring'''
						warnings.warn(
						    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,_SCREAMING_SNAKE_CASE						,)
						return self.image_processor





