
"""simple docstring"""


import argparse
import copy






def       _snake_case				(		snake_case__   :     List[Any]     ):
	A						   =   {}

	with open(snake_case__     ) 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				(		snake_case__   :     str	,      snake_case__   :     Any     ):

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

	A						   =   []

	A						   =   start_node

	A						   =   0
	while visiting not in first_solution:
		A						   =   1_0000
		for k in dict_of_neighbours[visiting]:
			if int(k[1]     ) < int(snake_case__     ) and k[0] not in first_solution:
				A						   =   k[1]
				A						   =   k[0]

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

	first_solution.append(snake_case__     )

	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_0000
	)
	return first_solution, distance_of_first_solution






def       _snake_case				(		snake_case__   :     Optional[int]	,      snake_case__   :     Tuple     ):
	A						   =   []

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

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

			A						   =   0

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

			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 snake_case__     : x[index_of_last_item_in_the_list]     )
	return neighborhood_of_solution






def       _snake_case				(		snake_case__   :     Tuple	,      snake_case__   :     Union[str, Any]	,      snake_case__   :     Union[str, Any]	,      snake_case__   :     Any	,      snake_case__   :     List[str]     ):
	A						   =   1
	A						   =   first_solution
	A						   =   []
	A						   =   distance_of_first_solution
	A						   =   solution

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

		A						   =   False
		while not found:
			A						   =   0
			while i < len(snake_case__     ):
				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(snake_case__     ) >= size:
			tabu_list.pop(0     )

		A						   =   count + 1

	return best_solution_ever, best_cost






def       _snake_case				(		snake_case__   :     List[str]=None     ):
	A						   =   generate_neighbours(args.File     )

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

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

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


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




"""simple docstring"""


import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ImageGPTImageProcessor







class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						def __init__( self     :      int  ,A_     :      Tuple  ,A_     :      str=7  ,A_     :      Tuple=3  ,A_     :      List[Any]=18  ,A_     :      List[str]=30  ,A_     :      Optional[Any]=400  ,A_     :      Any=True  ,A_     :      Optional[Any]=None  ,A_     :      List[str]=True  ,)     ->   str:
							A						   =   size if size is not None else {'height': 18, 'width': 18}
							A						   =   parent
							A						   =   batch_size
							A						   =   num_channels
							A						   =   image_size
							A						   =   min_resolution
							A						   =   max_resolution
							A						   =   do_resize
							A						   =   size
							A						   =   do_normalize




						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   List[Any]:
							return {
							    # here we create 2 clusters for the sake of simplicity
							    "clusters": np.asarray(
							        [
							            [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
							            [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
							        ]      ),
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							}







@require_torch
@require_vision
class 				lowerCAmelCase_				(      _lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    List[Any]      =    ImageGPTImageProcessor if is_vision_available() else None


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Any:
							A						   =   ImageGPTImageProcessingTester(self      )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Union[str, Any]:
							return self.image_processor_tester.prepare_image_processor_dict()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   List[Any]:
							A						   =   self.image_processing_class(**self.image_processor_dict      )
							self.assertTrue(hasattr(A_  ,'clusters'      )      )
							self.assertTrue(hasattr(A_  ,'do_resize'      )      )
							self.assertTrue(hasattr(A_  ,'size'      )      )
							self.assertTrue(hasattr(A_  ,'do_normalize'      )      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Tuple:
							A						   =   self.image_processing_class.from_dict(self.image_processor_dict      )
							self.assertEqual(image_processor.size  ,{'height': 18, 'width': 18}      )

							A						   =   self.image_processing_class.from_dict(self.image_processor_dict  ,size=42      )
							self.assertEqual(image_processor.size  ,{'height': 42, 'width': 42}      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   str:
							A						   =   self.image_processing_class(**self.image_processor_dict      )
							A						   =   json.loads(image_processor.to_json_string()      )
							for key, value in self.image_processor_dict.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,obj[key]      )      )
								else:
									self.assertEqual(obj[key]  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Union[str, Any]:
							A						   =   self.image_processing_class(**self.image_processor_dict      )

							with tempfile.TemporaryDirectory() as tmpdirname:
								A						   =   os.path.join(A_  ,'image_processor.json'      )
								image_processor_first.to_json_file(A_      )
								A						   =   self.image_processing_class.from_json_file(A_      ).to_dict()

							A						   =   image_processor_first.to_dict()
							for key, value in image_processor_first.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,image_processor_second[key]      )      )
								else:
									self.assertEqual(image_processor_first[key]  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   int:
							A						   =   self.image_processing_class(**self.image_processor_dict      )

							with tempfile.TemporaryDirectory() as tmpdirname:
								image_processor_first.save_pretrained(A_      )
								A						   =   self.image_processing_class.from_pretrained(A_      ).to_dict()

							A						   =   image_processor_first.to_dict()
							for key, value in image_processor_first.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,image_processor_second[key]      )      )
								else:
									self.assertEqual(image_processor_first[key]  ,A_      )




						@unittest.skip('ImageGPT requires clusters at initialization'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Union[str, Any]:
							pass






def       _snake_case				(		):
	A						   =   load_dataset('hf-internal-testing/fixtures_image_utils'	,      split='test'     )

	A						   =   Image.open(dataset[4]['file']     )
	A						   =   Image.open(dataset[5]['file']     )

	A						   =   [imagea, imagea]

	return images







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



						'''simple docstring'''


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   int:
							A						   =   ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'      )

							A						   =   prepare_images()

							# test non-batched
							A						   =   image_processing(images[0]  ,return_tensors='pt'      )

							self.assertIsInstance(encoding.input_ids  ,torch.LongTensor      )
							self.assertEqual(encoding.input_ids.shape  ,(1, 1024)      )

							A						   =   [306, 191, 191]
							self.assertEqual(encoding.input_ids[0, :3].tolist()  ,A_      )

							# test batched
							A						   =   image_processing(A_  ,return_tensors='pt'      )

							self.assertIsInstance(encoding.input_ids  ,torch.LongTensor      )
							self.assertEqual(encoding.input_ids.shape  ,(2, 1024)      )

							A						   =   [303, 13, 13]
							self.assertEqual(encoding.input_ids[1, -3:].tolist()  ,A_      )