


import argparse
import copy


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = {}

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

			return dict_of_neighbours


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			with open(_snake_case	) as f:
						__lowercase			       = f.read(1	)
			__lowercase			       = start_node

			__lowercase			       = []

			__lowercase			       = start_node

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

						first_solution.append(_snake_case	)
						__lowercase			       = distance_of_first_solution + int(_snake_case	)
						__lowercase			       = best_node

			first_solution.append(_snake_case	)

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

			__lowercase			       = (
			    distance_of_first_solution
			    + int(dict_of_neighbours[first_solution[-2]][position][1]	)
			    - 1_00_00
			)
			return first_solution, distance_of_first_solution


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = []

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

									__lowercase			       = copy.deepcopy(_snake_case	)
									__lowercase			       = kn
									__lowercase			       = n

									__lowercase			       = 0

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

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

			__lowercase			       = 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 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = 1
			__lowercase			       = first_solution
			__lowercase			       = []
			__lowercase			       = distance_of_first_solution
			__lowercase			       = solution

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

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

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

						__lowercase			       = count + 1

			return best_solution_ever, best_cost


def 		lowercase_	(  _UpperCamelCase=None	):


			'''simple docstring'''







			__lowercase			       = generate_neighbours(args.File	)

			__lowercase			       = generate_first_solution(
			    args.File							,      _snake_case	)

			__lowercase			       = 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__":
	a			:			List[str]												=					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 gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class 				a			(					lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   Any           =     KandinskyImgaImgPipeline
      UpperCamelCase							:   int           =     ["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
      UpperCamelCase							:   Optional[Any]           =     [
          """prompt""",
          """negative_prompt""",
          """image_embeds""",
          """negative_image_embeds""",
          """image""",
      ]
      UpperCamelCase							:   Union[str, Any]           =     [
          """generator""",
          """height""",
          """width""",
          """strength""",
          """guidance_scale""",
          """negative_prompt""",
          """num_inference_steps""",
          """return_dict""",
          """guidance_scale""",
          """num_images_per_prompt""",
          """output_type""",
          """return_dict""",
      ]
      UpperCamelCase							:   List[str]           =     False



      @property
      def  __snake_case							(   self      ):
          return 32



      @property
      def  __snake_case							(   self      ):
          return 32



      @property
      def  __snake_case							(   self      ):
          return self.time_input_dim



      @property
      def  __snake_case							(   self      ):
          return self.time_input_dim * 4



      @property
      def  __snake_case							(   self      ):
          return 100



      @property
      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      str     							=					XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'      )
          return tokenizer



      @property
      def  __snake_case							(   self      ):
          torch.manual_seed(0      )
          UpperCAmelCase__	:      Optional[int]     							=					MCLIPConfig(
              numDims=self.cross_attention_dim     ,	transformerDimensions=self.text_embedder_hidden_size     ,	hidden_size=self.text_embedder_hidden_size     ,	intermediate_size=37     ,	num_attention_heads=4     ,	num_hidden_layers=5     ,	vocab_size=1_005     ,	)

          UpperCAmelCase__	:      Tuple     							=					MultilingualCLIP(UpperCamelCase_      )
          UpperCAmelCase__	:      List[Any]     							=					text_encoder.eval()

          return text_encoder



      @property
      def  __snake_case							(   self      ):
          torch.manual_seed(0      )

          UpperCAmelCase__	:      Optional[Any]     							=					{
              'in_channels': 4,
              # Out channels is double in channels because predicts mean and variance
              'out_channels': 8,
              'addition_embed_type': 'text_image',
              'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
              'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
              'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
              'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
              'layers_per_block': 1,
              'encoder_hid_dim': self.text_embedder_hidden_size,
              'encoder_hid_dim_type': 'text_image_proj',
              'cross_attention_dim': self.cross_attention_dim,
              'attention_head_dim': 4,
              'resnet_time_scale_shift': 'scale_shift',
              'class_embed_type': None,
          }

          UpperCAmelCase__	:      Optional[Any]     							=					UNetaDConditionModel(**UpperCamelCase_      )
          return model



      @property
      def  __snake_case							(   self      ):
          return {
              "block_out_channels": [32, 64],
              "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
              "in_channels": 3,
              "latent_channels": 4,
              "layers_per_block": 1,
              "norm_num_groups": 8,
              "norm_type": "spatial",
              "num_vq_embeddings": 12,
              "out_channels": 3,
              "up_block_types": [
                  "AttnUpDecoderBlock2D",
                  "UpDecoderBlock2D",
              ],
              "vq_embed_dim": 4,
          }



      @property
      def  __snake_case							(   self      ):
          torch.manual_seed(0      )
          UpperCAmelCase__	:      Optional[Any]     							=					VQModel(**self.dummy_movq_kwargs      )
          return model



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					self.dummy_text_encoder
          UpperCAmelCase__	:      int     							=					self.dummy_tokenizer
          UpperCAmelCase__	:      int     							=					self.dummy_unet
          UpperCAmelCase__	:      Optional[Any]     							=					self.dummy_movq

          UpperCAmelCase__	:      Any     							=					{
              'num_train_timesteps': 1_000,
              'beta_schedule': 'linear',
              'beta_start': 0.00085,
              'beta_end': 0.012,
              'clip_sample': False,
              'set_alpha_to_one': False,
              'steps_offset': 0,
              'prediction_type': 'epsilon',
              'thresholding': False,
          }

          UpperCAmelCase__	:      List[Any]     							=					DDIMScheduler(**UpperCamelCase_      )

          UpperCAmelCase__	:      Tuple     							=					{
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'unet': unet,
              'scheduler': scheduler,
              'movq': movq,
          }

          return components



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=0      ):
          UpperCAmelCase__	:      int     							=					floats_tensor((1, self.cross_attention_dim)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
          UpperCAmelCase__	:      Optional[Any]     							=					floats_tensor((1, self.cross_attention_dim)     ,	rng=random.Random(seed + 1      )      ).to(UpperCamelCase_      )
          # create init_image
          UpperCAmelCase__	:      Optional[int]     							=					floats_tensor((1, 3, 64, 64)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
          UpperCAmelCase__	:      int     							=					image.cpu().permute(0     ,	2     ,	3     ,	1      )[0]
          UpperCAmelCase__	:      Union[str, Any]     							=					Image.fromarray(np.uinta(UpperCamelCase_      )      ).convert('RGB'      ).resize((256, 256)      )

          if str(UpperCamelCase_      ).startswith('mps'      ):
              UpperCAmelCase__	:      Dict     							=					torch.manual_seed(UpperCamelCase_      )
          else:
              UpperCAmelCase__	:      Optional[int]     							=					torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )
          UpperCAmelCase__	:      Tuple     							=					{
              'prompt': 'horse',
              'image': init_image,
              'image_embeds': image_embeds,
              'negative_image_embeds': negative_image_embeds,
              'generator': generator,
              'height': 64,
              'width': 64,
              'num_inference_steps': 10,
              'guidance_scale': 7.0,
              'strength': 0.2,
              'output_type': 'np',
          }
          return inputs



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[int]     							=					'cpu'

          UpperCAmelCase__	:      Any     							=					self.get_dummy_components()

          UpperCAmelCase__	:      Any     							=					self.pipeline_class(**UpperCamelCase_      )
          UpperCAmelCase__	:      Tuple     							=					pipe.to(UpperCamelCase_      )

          pipe.set_progress_bar_config(disable=UpperCamelCase_      )

          UpperCAmelCase__	:      List[Any]     							=					pipe(**self.get_dummy_inputs(UpperCamelCase_      )      )
          UpperCAmelCase__	:      Optional[int]     							=					output.images

          UpperCAmelCase__	:      Dict     							=					pipe(
              **self.get_dummy_inputs(UpperCamelCase_      )     ,	return_dict=UpperCamelCase_     ,	)[0]

          UpperCAmelCase__	:      Dict     							=					image[0, -3:, -3:, -1]
          UpperCAmelCase__	:      Any     							=					image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)

          UpperCAmelCase__	:      Any     							=					np.array(
              [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233]      )
          assert (
              np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
          ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
          assert (
              np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2
          ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''





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



      def  __snake_case							(   self      ):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      str     							=					load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/kandinsky/kandinsky_img2img_frog.npy'      )

          UpperCAmelCase__	:      List[Any]     							=					load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'      )
          UpperCAmelCase__	:      Any     							=					'A red cartoon frog, 4k'

          UpperCAmelCase__	:      Union[str, Any]     							=					KandinskyPriorPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1-prior'     ,	torch_dtype=torch.floataa      )
          pipe_prior.to(UpperCamelCase_      )

          UpperCAmelCase__	:      Any     							=					KandinskyImgaImgPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1'     ,	torch_dtype=torch.floataa      )
          UpperCAmelCase__	:      Optional[Any]     							=					pipeline.to(UpperCamelCase_      )

          pipeline.set_progress_bar_config(disable=UpperCamelCase_      )

          UpperCAmelCase__	:      List[Any]     							=					torch.Generator(device='cpu'      ).manual_seed(0      )
          UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[int]     							=					pipe_prior(
              UpperCamelCase_     ,	generator=UpperCamelCase_     ,	num_inference_steps=5     ,	negative_prompt=''     ,	).to_tuple()

          UpperCAmelCase__	:      Optional[Any]     							=					pipeline(
              UpperCamelCase_     ,	image=UpperCamelCase_     ,	image_embeds=UpperCamelCase_     ,	negative_image_embeds=UpperCamelCase_     ,	generator=UpperCamelCase_     ,	num_inference_steps=100     ,	height=768     ,	width=768     ,	strength=0.2     ,	output_type='np'     ,	)

          UpperCAmelCase__	:      Union[str, Any]     							=					output.images[0]

          assert image.shape == (768, 768, 3)

          assert_mean_pixel_difference(UpperCamelCase_     ,	UpperCamelCase_      )

