
"""simple docstring"""




import argparse
import copy



def 							a__ (				lowerCAmelCase	:       List[str]		):






     '''simple docstring'''
     UpperCAmelCase__							: Optional[int] 							=						{}

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

     return dict_of_neighbours



def 							a__ (				lowerCAmelCase	:       Tuple       ,    lowerCAmelCase	:       Union[str, Any]		):






     '''simple docstring'''

     with open(lowerCAmelCase		) as f:
          UpperCAmelCase__							: Optional[Any] 							=						f.read(1		)
     UpperCAmelCase__							: Any 							=						start_node

     UpperCAmelCase__							: List[str] 							=						[]

     UpperCAmelCase__							: Any 							=						start_node

     UpperCAmelCase__							: Optional[Any] 							=						0
     while visiting not in first_solution:
          UpperCAmelCase__							: Union[str, Any] 							=						1_0000
          for k in dict_of_neighbours[visiting]:
               if int(k[1]		) < int(lowerCAmelCase		) and k[0] not in first_solution:
                    UpperCAmelCase__							: int 							=						k[1]
                    UpperCAmelCase__							: List[Any] 							=						k[0]

          first_solution.append(lowerCAmelCase		)
          UpperCAmelCase__							: Dict 							=						distance_of_first_solution + int(lowerCAmelCase		)
          UpperCAmelCase__							: Tuple 							=						best_node

     first_solution.append(lowerCAmelCase		)

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

     UpperCAmelCase__							: Union[str, Any] 							=						(
         distance_of_first_solution
         + int(dict_of_neighbours[first_solution[-2]][position][1]		)
         - 1_0000
     )
     return first_solution, distance_of_first_solution



def 							a__ (				lowerCAmelCase	:       int       ,    lowerCAmelCase	:       Tuple		):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						[]

     for n in solution[1:-1]:
          UpperCAmelCase__							: Any 							=						solution.index(lowerCAmelCase		)
          for kn in solution[1:-1]:
               UpperCAmelCase__							: List[Any] 							=						solution.index(lowerCAmelCase		)
               if n == kn:
                    continue

               UpperCAmelCase__							: Optional[int] 							=						copy.deepcopy(lowerCAmelCase		)
               UpperCAmelCase__							: Dict 							=						kn
               UpperCAmelCase__							: Tuple 							=						n

               UpperCAmelCase__							: int 							=						0

               for k in _tmp[:-1]:
                    UpperCAmelCase__							: List[str] 							=						_tmp[_tmp.index(lowerCAmelCase		) + 1]
                    for i in dict_of_neighbours[k]:
                         if i[0] == next_node:
                              UpperCAmelCase__							: List[str] 							=						distance + int(i[1]		)
               _tmp.append(lowerCAmelCase		)

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

     UpperCAmelCase__							: Dict 							=						len(neighborhood_of_solution[0]		) - 1

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



def 							a__ (				lowerCAmelCase	:       List[str]       ,    lowerCAmelCase	:       Optional[int]       ,    lowerCAmelCase	:       Tuple       ,    lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       Optional[Any]		):






     '''simple docstring'''
     UpperCAmelCase__							: str 							=						1
     UpperCAmelCase__							: Tuple 							=						first_solution
     UpperCAmelCase__							: Optional[Any] 							=						[]
     UpperCAmelCase__							: Union[str, Any] 							=						distance_of_first_solution
     UpperCAmelCase__							: Any 							=						solution

     while count <= iters:
          UpperCAmelCase__							: Union[str, Any] 							=						find_neighborhood(lowerCAmelCase       ,    lowerCAmelCase		)
          UpperCAmelCase__							: List[Any] 							=						0
          UpperCAmelCase__							: Tuple 							=						neighborhood[index_of_best_solution]
          UpperCAmelCase__							: Optional[int] 							=						len(lowerCAmelCase		) - 1

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

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

          UpperCAmelCase__							: List[str] 							=						count + 1

     return best_solution_ever, best_cost



def 							a__ (				lowerCAmelCase	:       str=None		):






     '''simple docstring'''
     UpperCAmelCase__							: Optional[int] 							=						generate_neighbours(args.File		)

     UpperCAmelCase__							,	UpperCAmelCase__							: Tuple 							=						generate_first_solution(
         args.File       ,    lowerCAmelCase		)

     UpperCAmelCase__							,	UpperCAmelCase__							: Optional[int] 							=						tabu_search(
         lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    args.Iterations       ,    args.Size       ,    )

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


if __name__ == "__main__":
    A__      :	int          =    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 numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
    import onnxruntime as ort







class   _lowercase						(       lowerCAmelCase_	,   unittest.TestCase				):


     '''simple docstring'''





     pass







@nightly
@require_onnxruntime
@require_torch_gpu
class   _lowercase						(       unittest.TestCase				):


     '''simple docstring'''





     @property
     def  lowerCAmelCase__       (      self  )->					int:
          return (
              "CUDAExecutionProvider",
              {
                  "gpu_mem_limit": "15000000000",  # 15GB
                  "arena_extend_strategy": "kSameAsRequested",
              },
          )



     @property
     def  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: Tuple 							=						ort.SessionOptions()
          UpperCAmelCase__							: List[str] 							=						False
          return options



     def  lowerCAmelCase__       (      self  )->					List[str]:
          UpperCAmelCase__							: Union[str, Any] 							=						load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo.png"  )
          UpperCAmelCase__							: int 							=						load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"  )
          UpperCAmelCase__							: str 							=						OnnxStableDiffusionInpaintPipeline.from_pretrained(
              "runwayml/stable-diffusion-inpainting" ,			revision="onnx" ,			safety_checker=__UpperCamelCase ,			feature_extractor=__UpperCamelCase ,			provider=self.gpu_provider ,			sess_options=self.gpu_options ,			)
          pipe.set_progress_bar_config(disable=__UpperCamelCase  )

          UpperCAmelCase__							: int 							=						"A red cat sitting on a park bench"

          UpperCAmelCase__							: Tuple 							=						np.random.RandomState(0  )
          UpperCAmelCase__							: Any 							=						pipe(
              prompt=__UpperCamelCase ,			image=__UpperCamelCase ,			mask_image=__UpperCamelCase ,			guidance_scale=7.5 ,			num_inference_steps=10 ,			generator=__UpperCamelCase ,			output_type="np" ,			)
          UpperCAmelCase__							: Tuple 							=						output.images
          UpperCAmelCase__							: Dict 							=						images[0, 2_55:2_58, 2_55:2_58, -1]

          assert images.shape == (1, 5_12, 5_12, 3)
          UpperCAmelCase__							: Union[str, Any] 							=						np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]  )

          assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3



     def  lowerCAmelCase__       (      self  )->					Tuple:
          UpperCAmelCase__							: Tuple 							=						load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo.png"  )
          UpperCAmelCase__							: Tuple 							=						load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"  )
          UpperCAmelCase__							: Optional[Any] 							=						LMSDiscreteScheduler.from_pretrained(
              "runwayml/stable-diffusion-inpainting" ,			subfolder="scheduler" ,			revision="onnx"  )
          UpperCAmelCase__							: Optional[Any] 							=						OnnxStableDiffusionInpaintPipeline.from_pretrained(
              "runwayml/stable-diffusion-inpainting" ,			revision="onnx" ,			scheduler=__UpperCamelCase ,			safety_checker=__UpperCamelCase ,			feature_extractor=__UpperCamelCase ,			provider=self.gpu_provider ,			sess_options=self.gpu_options ,			)
          pipe.set_progress_bar_config(disable=__UpperCamelCase  )

          UpperCAmelCase__							: int 							=						"A red cat sitting on a park bench"

          UpperCAmelCase__							: List[str] 							=						np.random.RandomState(0  )
          UpperCAmelCase__							: str 							=						pipe(
              prompt=__UpperCamelCase ,			image=__UpperCamelCase ,			mask_image=__UpperCamelCase ,			guidance_scale=7.5 ,			num_inference_steps=20 ,			generator=__UpperCamelCase ,			output_type="np" ,			)
          UpperCAmelCase__							: List[str] 							=						output.images
          UpperCAmelCase__							: List[Any] 							=						images[0, 2_55:2_58, 2_55:2_58, -1]

          assert images.shape == (1, 5_12, 5_12, 3)
          UpperCAmelCase__							: int 							=						np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]  )

          assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3



