


"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 					__magic_name__		(			__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						unittest.TestCase     ):
       __A			:      List[str]        =   StableDiffusionInpaintPipeline
       __A			:      int        =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
       __A			:      Optional[Any]        =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       __A			:      Dict        =   frozenset(
           []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       __A			:      str        =   frozenset([]     )






       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              torch.manual_seed(0				)
              lowercase		:Optional[Any]       							=				UNetaDConditionModel(
                  block_out_channels=(3_2, 6_4)  ,     layers_per_block=2  ,     sample_size=3_2  ,     in_channels=9  ,     out_channels=4  ,     down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')  ,     up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')  ,     cross_attention_dim=3_2  ,     attention_head_dim=(2, 4)  ,     use_linear_projection=UpperCamelCase_  ,     )
              lowercase		:Any       							=				PNDMScheduler(skip_prk_steps=UpperCamelCase_				)
              torch.manual_seed(0				)
              lowercase		:int       							=				AutoencoderKL(
                  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=4  ,     sample_size=1_2_8  ,     )
              torch.manual_seed(0				)
              lowercase		:Tuple       							=				CLIPTextConfig(
                  bos_token_id=0  ,     eos_token_id=2  ,     hidden_size=3_2  ,     intermediate_size=3_7  ,     layer_norm_eps=1e-0_5  ,     num_attention_heads=4  ,     num_hidden_layers=5  ,     pad_token_id=1  ,     vocab_size=1_0_0_0  ,     hidden_act='''gelu'''  ,     projection_dim=5_1_2  ,     )
              lowercase		:Dict       							=				CLIPTextModel(UpperCamelCase_				)
              lowercase		:int       							=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

              lowercase		:int       							=				{
                  '''unet''': unet,
                  '''scheduler''': scheduler,
                  '''vae''': vae,
                  '''text_encoder''': text_encoder,
                  '''tokenizer''': tokenizer,
                  '''safety_checker''': None,
                  '''feature_extractor''': None,
              }
              return components






       def  __snake_case						(   self  :    Any  ,     snake_case__  :    Optional[Any]  ,     snake_case__  :    List[Any]=0				):

              '''simple docstring'''






              lowercase		:List[Any]       							=				floats_tensor((1, 3, 3_2, 3_2)  ,     rng=random.Random(UpperCamelCase_				)				).to(UpperCamelCase_				)
              lowercase		:Optional[int]       							=				image.cpu().permute(0  ,     2  ,     3  ,     1				)[0]
              lowercase		:Optional[Any]       							=				Image.fromarray(np.uinta(UpperCamelCase_				)				).convert('''RGB'''				).resize((6_4, 6_4)				)
              lowercase		:Any       							=				Image.fromarray(np.uinta(image + 4				)				).convert('''RGB'''				).resize((6_4, 6_4)				)
              if str(UpperCamelCase_				).startswith('''mps'''				):
                     lowercase		:List[str]       							=				torch.manual_seed(UpperCamelCase_				)
              else:
                     lowercase		:Tuple       							=				torch.Generator(device=UpperCamelCase_				).manual_seed(UpperCamelCase_				)
              lowercase		:List[str]       							=				{
                  '''prompt''': '''A painting of a squirrel eating a burger''',
                  '''image''': init_image,
                  '''mask_image''': mask_image,
                  '''generator''': generator,
                  '''num_inference_steps''': 2,
                  '''guidance_scale''': 6.0,
                  '''output_type''': '''numpy''',
              }
              return inputs






       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              lowercase		:Any       							=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
              lowercase		:Any       							=				self.get_dummy_components()
              lowercase		:Any       							=				StableDiffusionInpaintPipeline(**UpperCamelCase_				)
              lowercase		:Tuple       							=				sd_pipe.to(UpperCamelCase_				)
              sd_pipe.set_progress_bar_config(disable=UpperCamelCase_				)

              lowercase		:str       							=				self.get_dummy_inputs(UpperCamelCase_				)
              lowercase		:Any       							=				sd_pipe(**UpperCamelCase_				).images
              lowercase		:List[str]       							=				image[0, -3:, -3:, -1]

              assert image.shape == (1, 6_4, 6_4, 3)
              lowercase		:Dict       							=				np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]				)

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







       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              super().test_inference_batch_single_identical(expected_max_diff=3e-3				)





@slow
@require_torch_gpu
class 					__magic_name__		(			unittest.TestCase     ):






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()






       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              lowercase		:List[str]       							=				load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                  '''/sd2-inpaint/init_image.png'''				)
              lowercase		:List[str]       							=				load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''				)
              lowercase		:Optional[int]       							=				load_numpy(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
                  '''/yellow_cat_sitting_on_a_park_bench.npy'''				)

              lowercase		:Tuple       							=				'''stabilityai/stable-diffusion-2-inpainting'''
              lowercase		:str       							=				StableDiffusionInpaintPipeline.from_pretrained(UpperCamelCase_  ,     safety_checker=UpperCamelCase_				)
              pipe.to(UpperCamelCase_				)
              pipe.set_progress_bar_config(disable=UpperCamelCase_				)
              pipe.enable_attention_slicing()

              lowercase		:Dict       							=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

              lowercase		:Any       							=				torch.manual_seed(0				)
              lowercase		:Optional[int]       							=				pipe(
                  prompt=UpperCamelCase_  ,     image=UpperCamelCase_  ,     mask_image=UpperCamelCase_  ,     generator=UpperCamelCase_  ,     output_type='''np'''  ,     )
              lowercase		:List[str]       							=				output.images[0]

              assert image.shape == (5_1_2, 5_1_2, 3)
              assert np.abs(expected_image - image				).max() < 9e-3






       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                  '''/sd2-inpaint/init_image.png'''				)
              lowercase		:List[Any]       							=				load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''				)
              lowercase		:Optional[int]       							=				load_numpy(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
                  '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''				)

              lowercase		:List[Any]       							=				'''stabilityai/stable-diffusion-2-inpainting'''
              lowercase		:Union[str, Any]       							=				StableDiffusionInpaintPipeline.from_pretrained(
                  UpperCamelCase_  ,     torch_dtype=torch.floataa  ,     safety_checker=UpperCamelCase_  ,     )
              pipe.to(UpperCamelCase_				)
              pipe.set_progress_bar_config(disable=UpperCamelCase_				)
              pipe.enable_attention_slicing()

              lowercase		:Optional[Any]       							=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

              lowercase		:Any       							=				torch.manual_seed(0				)
              lowercase		:Tuple       							=				pipe(
                  prompt=UpperCamelCase_  ,     image=UpperCamelCase_  ,     mask_image=UpperCamelCase_  ,     generator=UpperCamelCase_  ,     output_type='''np'''  ,     )
              lowercase		:str       							=				output.images[0]

              assert image.shape == (5_1_2, 5_1_2, 3)
              assert np.abs(expected_image - image				).max() < 5e-1







       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              torch.cuda.empty_cache()
              torch.cuda.reset_max_memory_allocated()
              torch.cuda.reset_peak_memory_stats()

              lowercase		:Optional[Any]       							=				load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                  '''/sd2-inpaint/init_image.png'''				)
              lowercase		:Optional[Any]       							=				load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''				)

              lowercase		:Optional[Any]       							=				'''stabilityai/stable-diffusion-2-inpainting'''
              lowercase		:int       							=				PNDMScheduler.from_pretrained(UpperCamelCase_  ,     subfolder='''scheduler'''				)
              lowercase		:int       							=				StableDiffusionInpaintPipeline.from_pretrained(
                  UpperCamelCase_  ,     safety_checker=UpperCamelCase_  ,     scheduler=UpperCamelCase_  ,     torch_dtype=torch.floataa  ,     )
              pipe.to(UpperCamelCase_				)
              pipe.set_progress_bar_config(disable=UpperCamelCase_				)
              pipe.enable_attention_slicing(1				)
              pipe.enable_sequential_cpu_offload()

              lowercase		:Dict       							=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

              lowercase		:int       							=				torch.manual_seed(0				)
              lowercase		:int       							=				pipe(
                  prompt=UpperCamelCase_  ,     image=UpperCamelCase_  ,     mask_image=UpperCamelCase_  ,     generator=UpperCamelCase_  ,     num_inference_steps=2  ,     output_type='''np'''  ,     )

              lowercase		:Union[str, Any]       							=				torch.cuda.max_memory_allocated()
              # make sure that less than 2.65 GB is allocated
              assert mem_bytes < 2.65 * 1_0**9





"""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())

