

'''simple docstring'''





from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


UpperCamelCase_							=					logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCamelCase_							=					"\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n        >>> pipe_prior.to(\"cuda\")\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n        >>> pipe.to(\"cuda\")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save(\"cat.png\")\n        ```\n"






def 							_lowerCAmelCase (      __magic_name__	:    List[str]      ,   __magic_name__	:    str      ,   __magic_name__	:    Optional[Any]=8	)							->     Tuple:
						lowercase   :       Optional[Any]								=height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						lowercase   :       List[Any]								=width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor





class 		__SCREAMING_SNAKE_CASE       ( __UpperCAmelCase			):







				def __init__(					self    :		Optional[Any]		,				UpperCAmelCase__    :		UNetaDConditionModel		,				UpperCAmelCase__    :		DDPMScheduler		,				UpperCAmelCase__    :		VQModel		,				):
										'''simple docstring'''
										super().__init__()

										self.register_modules(
										    unet=lowerCAmelCase_		,				scheduler=lowerCAmelCase_		,				movq=lowerCAmelCase_		,				)
										lowercase   :       Tuple								=2 ** (len(self.movq.config.block_out_channels      ) - 1)







				def    lowerCamelCase_  (					self    :		Dict		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		List[str]		,				UpperCAmelCase__    :		List[str]		,				UpperCAmelCase__    :		Tuple		,				UpperCAmelCase__    :		Tuple		,				UpperCAmelCase__    :		Union[str, Any]      ):
										'''simple docstring'''







										if latents is None:
																lowercase   :       Any								=randn_tensor(lowerCAmelCase_		,				generator=lowerCAmelCase_		,				device=lowerCAmelCase_		,				dtype=lowerCAmelCase_      )
										else:
																if latents.shape != shape:
																						raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''      )
																lowercase   :       List[Any]								=latents.to(lowerCAmelCase_      )

										lowercase   :       Optional[Any]								=latents * scheduler.init_noise_sigma
										return latents







				def    lowerCamelCase_  (					self    :		Union[str, Any]		,				UpperCAmelCase__    :		Optional[Any]=0      ):
										'''simple docstring'''







										if is_accelerate_available():
																from accelerate import cpu_offload
										else:
																raise ImportError('''Please install accelerate via `pip install accelerate`'''      )

										lowercase   :       Union[str, Any]								=torch.device(F'''cuda:{gpu_id}'''      )

										lowercase   :       str								=[
										    self.unet,
										    self.movq,
										]
										for cpu_offloaded_model in models:
																if cpu_offloaded_model is not None:
																						cpu_offload(lowerCAmelCase_		,				lowerCAmelCase_      )







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		Optional[int]=0      ):
										'''simple docstring'''







										if is_accelerate_available() and is_accelerate_version('''>='''		,				'''0.17.0.dev0'''      ):
																from accelerate import cpu_offload_with_hook
										else:
																raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''      )

										lowercase   :       List[Any]								=torch.device(F'''cuda:{gpu_id}'''      )

										if self.device.type != "cpu":
																self.to('''cpu'''		,				silence_dtype_warnings=lowerCAmelCase_      )
																torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

										lowercase   :       Optional[int]								=None
										for cpu_offloaded_model in [self.unet, self.movq]:
																lowercase		,  lowercase   :       Tuple								=cpu_offload_with_hook(lowerCAmelCase_		,				lowerCAmelCase_		,				prev_module_hook=lowerCAmelCase_      )

										# We'll offload the last model manually.
										lowercase   :       Tuple								=hook







				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''







										if not hasattr(self.unet		,				'''_hf_hook'''      ):
																return self.device
										for module in self.unet.modules():
																if (
																    hasattr(lowerCAmelCase_		,				'''_hf_hook'''      )
																    and hasattr(module._hf_hook		,				'''execution_device'''      )
																    and module._hf_hook.execution_device is not None
																):
																						return torch.device(module._hf_hook.execution_device      )
										return self.device







				@torch.no_grad()
				@replace_example_docstring(lowerCAmelCase_      )
				def __call__(					self    :		Dict		,				UpperCAmelCase__    :		Union[torch.FloatTensor, List[torch.FloatTensor]]		,				UpperCAmelCase__    :		Union[torch.FloatTensor, List[torch.FloatTensor]]		,				UpperCAmelCase__    :		int = 512		,				UpperCAmelCase__    :		int = 512		,				UpperCAmelCase__    :		int = 100		,				UpperCAmelCase__    :		float = 4.0		,				UpperCAmelCase__    :		int = 1		,				UpperCAmelCase__    :		Optional[Union[torch.Generator, List[torch.Generator]]] = None		,				UpperCAmelCase__    :		Optional[torch.FloatTensor] = None		,				UpperCAmelCase__    :		Optional[str] = "pil"		,				UpperCAmelCase__    :		bool = True		,				):
										'''simple docstring'''
										lowercase   :       Tuple								=self._execution_device

										lowercase   :       str								=guidance_scale > 1.0

										if isinstance(lowerCAmelCase_		,				lowerCAmelCase_      ):
																lowercase   :       Dict								=torch.cat(lowerCAmelCase_		,				dim=0      )
										lowercase   :       int								=image_embeds.shape[0] * num_images_per_prompt
										if isinstance(lowerCAmelCase_		,				lowerCAmelCase_      ):
																lowercase   :       Union[str, Any]								=torch.cat(lowerCAmelCase_		,				dim=0      )

										if do_classifier_free_guidance:
																lowercase   :       Optional[Any]								=image_embeds.repeat_interleave(lowerCAmelCase_		,				dim=0      )
																lowercase   :       Dict								=negative_image_embeds.repeat_interleave(lowerCAmelCase_		,				dim=0      )

										lowercase   :       Optional[Any]								=torch.cat([negative_image_embeds, image_embeds]		,				dim=0      ).to(dtype=self.unet.dtype		,				device=lowerCAmelCase_      )

										self.scheduler.set_timesteps(lowerCAmelCase_		,				device=lowerCAmelCase_      )
										lowercase   :       Optional[int]								=self.scheduler.timesteps

										lowercase   :       Union[str, Any]								=self.unet.config.in_channels

										lowercase		,  lowercase   :       List[Any]								=downscale_height_and_width(lowerCAmelCase_		,				lowerCAmelCase_		,				self.movq_scale_factor      )

										# create initial latent
										lowercase   :       Optional[Any]								=self.prepare_latents(
										    (batch_size, num_channels_latents, height, width)		,				image_embeds.dtype		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				self.scheduler		,				)

										for i, t in enumerate(self.progress_bar(lowerCAmelCase_      )      ):
																# expand the latents if we are doing classifier free guidance
																lowercase   :       Any								=torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents

																lowercase   :       Any								={'''image_embeds''': image_embeds}
																lowercase   :       Union[str, Any]								=self.unet(
																    sample=lowerCAmelCase_		,				timestep=lowerCAmelCase_		,				encoder_hidden_states=lowerCAmelCase_		,				added_cond_kwargs=lowerCAmelCase_		,				return_dict=lowerCAmelCase_		,				)[0]

																if do_classifier_free_guidance:
																						lowercase		,  lowercase   :       List[str]								=noise_pred.split(latents.shape[1]		,				dim=1      )
																						lowercase		,  lowercase   :       str								=noise_pred.chunk(2      )
																						lowercase		,  lowercase   :       Union[str, Any]								=variance_pred.chunk(2      )
																						lowercase   :       Dict								=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																						lowercase   :       List[Any]								=torch.cat([noise_pred, variance_pred_text]		,				dim=1      )

																if not (
																    hasattr(self.scheduler.config		,				'''variance_type'''      )
																    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																):
																						lowercase		,  lowercase   :       Any								=noise_pred.split(latents.shape[1]		,				dim=1      )

																# compute the previous noisy sample x_t -> x_t-1
																lowercase   :       str								=self.scheduler.step(
																    lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				generator=lowerCAmelCase_		,				)[0]
										# post-processing
										lowercase   :       Union[str, Any]								=self.movq.decode(lowerCAmelCase_		,				force_not_quantize=lowerCAmelCase_      )['''sample''']

										if output_type not in ["pt", "np", "pil"]:
																raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''      )

										if output_type in ["np", "pil"]:
																lowercase   :       Union[str, Any]								=image * 0.5 + 0.5
																lowercase   :       List[str]								=image.clamp(0		,				1      )
																lowercase   :       Union[str, Any]								=image.cpu().permute(0		,				2		,				3		,				1      ).float().numpy()

										if output_type == "pil":
																lowercase   :       Tuple								=self.numpy_to_pil(lowerCAmelCase_      )

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=lowerCAmelCase_      )








'''simple docstring'''





import argparse
import copy






def 							_lowerCAmelCase (      __magic_name__	:    List[str]	)							->     Union[str, Any]:
						lowercase   :       int								={}

						with open(__magic_name__	) 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   :       Tuple								=_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   :       Union[str, Any]								=_list
																		else:
																								dict_of_neighbours[line.split()[1]].append(
																								    [line.split()[0], line.split()[2]]	)

						return dict_of_neighbours






def 							_lowerCAmelCase (      __magic_name__	:    Optional[int]      ,   __magic_name__	:    List[Any]	)							->     str:

						with open(__magic_name__	) as f:
												lowercase   :       Optional[int]								=f.read(1	)
						lowercase   :       List[Any]								=start_node

						lowercase   :       List[Any]								=[]

						lowercase   :       str								=start_node

						lowercase   :       str								=0
						while visiting not in first_solution:
												lowercase   :       Optional[int]								=10000
												for k in dict_of_neighbours[visiting]:
																		if int(k[1]	) < int(__magic_name__	) and k[0] not in first_solution:
																								lowercase   :       List[Any]								=k[1]
																								lowercase   :       str								=k[0]

												first_solution.append(__magic_name__	)
												lowercase   :       Any								=distance_of_first_solution + int(__magic_name__	)
												lowercase   :       Optional[int]								=best_node

						first_solution.append(__magic_name__	)

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

						lowercase   :       str								=(
						    distance_of_first_solution
						    + int(dict_of_neighbours[first_solution[-2]][position][1]	)
						    - 10000
						)
						return first_solution, distance_of_first_solution






def 							_lowerCAmelCase (      __magic_name__	:    str      ,   __magic_name__	:    Any	)							->     Tuple:
						lowercase   :       Tuple								=[]

						for n in solution[1:-1]:
												lowercase   :       Dict								=solution.index(__magic_name__	)
												for kn in solution[1:-1]:
																		lowercase   :       Tuple								=solution.index(__magic_name__	)
																		if n == kn:
																								continue

																		lowercase   :       Union[str, Any]								=copy.deepcopy(__magic_name__	)
																		lowercase   :       Optional[int]								=kn
																		lowercase   :       List[Any]								=n

																		lowercase   :       List[Any]								=0

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

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

						lowercase   :       Union[str, Any]								=len(neighborhood_of_solution[0]	) - 1

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






def 							_lowerCAmelCase (      __magic_name__	:    Any      ,   __magic_name__	:    str      ,   __magic_name__	:    List[Any]      ,   __magic_name__	:    Tuple      ,   __magic_name__	:    Dict	)							->     Union[str, Any]:
						lowercase   :       str								=1
						lowercase   :       List[Any]								=first_solution
						lowercase   :       Any								=[]
						lowercase   :       str								=distance_of_first_solution
						lowercase   :       str								=solution

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

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

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

												lowercase   :       Optional[int]								=count + 1

						return best_solution_ever, best_cost






def 							_lowerCAmelCase (      __magic_name__	:    str=None	)							->     Tuple:
						lowercase   :       List[str]								=generate_neighbours(args.File	)

						lowercase		,  lowercase   :       Optional[Any]								=generate_first_solution(
						    args.File      ,   __magic_name__	)

						lowercase		,  lowercase   :       int								=tabu_search(
						    __magic_name__      ,   __magic_name__      ,   __magic_name__      ,   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())





