


"""simple docstring"""



import argparse
import copy






def       lowerCamelCase					(a_						:Union[str, Any])     ->							Tuple:
       lowercase		:Dict       							=				{}

       with open(a_) 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		: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:
                            lowercase		:List[Any]       							=				[]
                            _list.append([line.split()[0], line.split()[2]])
                            lowercase		:Dict       							=				_list
                     else:
                            dict_of_neighbours[line.split()[1]].append(
                                [line.split()[0], line.split()[2]])

       return dict_of_neighbours






def       lowerCamelCase					(a_						:Any				,				a_						:Dict)     ->							Any:

       with open(a_) as f:
              lowercase		:Any       							=				f.read(1)
       lowercase		:Any       							=				start_node

       lowercase		:Any       							=				[]

       lowercase		:Union[str, Any]       							=				start_node

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

              first_solution.append(a_)
              lowercase		:int       							=				distance_of_first_solution + int(a_)
              lowercase		:Dict       							=				best_node

       first_solution.append(a_)

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

       lowercase		:List[str]       							=				(
           distance_of_first_solution
           + int(dict_of_neighbours[first_solution[-2]][position][1])
           - 1_0000
       )
       return first_solution, distance_of_first_solution






def       lowerCamelCase					(a_						:List[str]				,				a_						:str)     ->							str:
       lowercase		:Any       							=				[]

       for n in solution[1:-1]:
              lowercase		:int       							=				solution.index(a_)
              for kn in solution[1:-1]:
                     lowercase		:Union[str, Any]       							=				solution.index(a_)
                     if n == kn:
                            continue

                     lowercase		:int       							=				copy.deepcopy(a_)
                     lowercase		:str       							=				kn
                     lowercase		:List[Any]       							=				n

                     lowercase		:int       							=				0

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

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

       lowercase		:Dict       							=				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_						:int				,				a_						:Optional[int]				,				a_						:List[Any]				,				a_						:Any				,				a_						:Optional[Any])     ->							List[Any]:
       lowercase		:Union[str, Any]       							=				1
       lowercase		:str       							=				first_solution
       lowercase		:int       							=				[]
       lowercase		:int       							=				distance_of_first_solution
       lowercase		:List[str]       							=				solution

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

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

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

              lowercase		:int       							=				count + 1

       return best_solution_ever, best_cost






def       lowerCamelCase					(a_						:Tuple=None)     ->							Any:
       lowercase		:Tuple       							=				generate_neighbours(args.File)

       lowercase							,  lowercase		:List[str]       							=				generate_first_solution(
           args.File				,				a_)

       lowercase							,  lowercase		:Union[str, Any]       							=				tabu_search(
           a_				,				a_				,				a_				,				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())





"""simple docstring"""



import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()
class 					__magic_name__		(			__UpperCAmelCase			,						__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      List[str]        =   VQModel
       __A			:      Any        =   "sample"






       @property
       def  __snake_case						(   self  :    int  ,     snake_case__  :    int=(3_2, 3_2)				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				4
              lowercase		:Tuple       							=				3

              lowercase		:List[str]       							=				floats_tensor((batch_size, num_channels) + sizes				).to(snake_case__				)

              return {"sample": image}






       @property
       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              return (3, 3_2, 3_2)






       @property
       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              return (3, 3_2, 3_2)






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				{
                  '''block_out_channels''': [3_2, 6_4],
                  '''in_channels''': 3,
                  '''out_channels''': 3,
                  '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
                  '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
                  '''latent_channels''': 3,
              }
              lowercase		:Dict       							=				self.dummy_input
              return init_dict, inputs_dict






       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              pass






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              pass






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase							,  lowercase		:Optional[int]       							=				VQModel.from_pretrained('''fusing/vqgan-dummy'''  ,     output_loading_info=snake_case__				)
              self.assertIsNotNone(snake_case__				)
              self.assertEqual(len(loading_info['''missing_keys''']				)  ,     0				)

              model.to(snake_case__				)
              lowercase		:List[str]       							=				model(**self.dummy_input				)

              assert image is not None, "Make sure output is not None"







       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				VQModel.from_pretrained('''fusing/vqgan-dummy'''				)
              model.to(snake_case__				).eval()

              torch.manual_seed(0				)
              if torch.cuda.is_available():
                     torch.cuda.manual_seed_all(0				)

              lowercase		:List[Any]       							=				torch.randn(1  ,     model.config.in_channels  ,     model.config.sample_size  ,     model.config.sample_size				)
              lowercase		:int       							=				image.to(snake_case__				)
              with torch.no_grad():
                     lowercase		:str       							=				model(snake_case__				).sample

              lowercase		:List[Any]       							=				output[0, -1, -3:, -3:].flatten().cpu()
              # fmt: off
              lowercase		:Tuple       							=				torch.tensor([-0.01_53, -0.40_44, -0.18_80, -0.51_61, -0.24_18, -0.40_72, -0.16_12, -0.06_33, -0.01_43]				)
              # fmt: on
              self.assertTrue(torch.allclose(snake_case__  ,     snake_case__  ,     atol=1e-3				)				)

