


import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class   A__ (   unittest.TestCase     ):







    """simple docstring"""

    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )
         snake_case					=		UNetaDModel(
             block_out_channels=(3_2, 6_4)      , layers_per_block=2      , sample_size=3_2      , in_channels=3      , out_channels=3      , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')      , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')      , )
         return model




    def 		a_      (	self   ):
         snake_case					=		self.dummy_uncond_unet
         snake_case					=		PNDMScheduler()

         snake_case					=		PNDMPipeline(unet=_A      , scheduler=_A   )
         pndm.to(_A   )
         pndm.set_progress_bar_config(disable=_A   )

         snake_case					=		torch.manual_seed(0   )
         snake_case					=		pndm(generator=_A      , num_inference_steps=2_0      , output_type='''numpy'''   ).images

         snake_case					=		torch.manual_seed(0   )
         snake_case					=		pndm(generator=_A      , num_inference_steps=2_0      , output_type='''numpy'''      , return_dict=_A   )[0]

         snake_case					=		image[0, -3:, -3:, -1]
         snake_case					=		image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 3_2, 3_2, 3)
         snake_case					=		np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2






@slow
@require_torch
class   A__ (   unittest.TestCase     ):







    """simple docstring"""

    def 		a_      (	self   ):
         snake_case					=		'google/ddpm-cifar10-32'

         snake_case					=		UNetaDModel.from_pretrained(_A   )
         snake_case					=		PNDMScheduler()

         snake_case					=		PNDMPipeline(unet=_A      , scheduler=_A   )
         pndm.to(_A   )
         pndm.set_progress_bar_config(disable=_A   )
         snake_case					=		torch.manual_seed(0   )
         snake_case					=		pndm(generator=_A      , output_type='''numpy'''   ).images

         snake_case					=		image[0, -3:, -3:, -1]

         assert image.shape == (1, 3_2, 3_2, 3)
         snake_case					=		np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2








import argparse
import copy
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    {}

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

     return dict_of_neighbours
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     with open(UpperCamelCase_    ) as f:
          snake_case					=    f.read(1    )
     snake_case					=    start_node

     snake_case					=    []

     snake_case					=    start_node

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

          first_solution.append(UpperCamelCase_    )
          snake_case					=    distance_of_first_solution + int(UpperCamelCase_    )
          snake_case					=    best_node

     first_solution.append(UpperCamelCase_    )

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

     snake_case					=    (
         distance_of_first_solution
         + int(dict_of_neighbours[first_solution[-2]][position][1]    )
         - 1_00_00
     )
     return first_solution, distance_of_first_solution
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    []

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

               snake_case					=    copy.deepcopy(UpperCamelCase_    )
               snake_case					=    kn
               snake_case					=    n

               snake_case					=    0

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

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

     snake_case					=    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       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    1
     snake_case					=    first_solution
     snake_case					=    []
     snake_case					=    distance_of_first_solution
     snake_case					=    solution

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

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

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

          snake_case					=    count + 1

     return best_solution_ever, best_cost
def       UpperCAmelCase__						(UpperCamelCase_=None    ):



     """simple docstring"""






     snake_case					=    generate_neighbours(args.File    )

     snake_case			,  snake_case					=    generate_first_solution(
         args.File							,UpperCamelCase_    )

     snake_case			,  snake_case					=    tabu_search(
         UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,args.Iterations							,args.Size							,)

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


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   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())




