

import argparse
import copy






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					str:
	__lowerCamelCase    :    Union[str, Any] 	=			{}

	with open(lowerCamelCase__      ) as f:
		for line in f:
			if line.split()[0] not in dict_of_neighbours:
				__lowerCamelCase    :    Dict 	=			[]
				_list.append([line.split()[1], line.split()[2]]      )
				__lowerCamelCase    :    Optional[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:
				__lowerCamelCase    :    Any 	=			[]
				_list.append([line.split()[0], line.split()[2]]      )
				__lowerCamelCase    :    Optional[Any] 	=			_list
			else:
				dict_of_neighbours[line.split()[1]].append(
				    [line.split()[0], line.split()[2]]      )

	return dict_of_neighbours






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					int:

	with open(lowerCamelCase__      ) as f:
		__lowerCamelCase    :    List[str] 	=			f.read(1      )
	__lowerCamelCase    :    Union[str, Any] 	=			start_node

	__lowerCamelCase    :    Tuple 	=			[]

	__lowerCamelCase    :    Optional[int] 	=			start_node

	__lowerCamelCase    :    Dict 	=			0
	while visiting not in first_solution:
		__lowerCamelCase    :    List[str] 	=			1_0_0_0_0
		for k in dict_of_neighbours[visiting]:
			if int(k[1]      ) < int(lowerCamelCase__      ) and k[0] not in first_solution:
				__lowerCamelCase    :    List[str] 	=			k[1]
				__lowerCamelCase    :    Tuple 	=			k[0]

		first_solution.append(lowerCamelCase__      )
		__lowerCamelCase    :    Dict 	=			distance_of_first_solution + int(lowerCamelCase__      )
		__lowerCamelCase    :    int 	=			best_node

	first_solution.append(lowerCamelCase__      )

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

	__lowerCamelCase    :    List[Any] 	=			(
	    distance_of_first_solution
	    + int(dict_of_neighbours[first_solution[-2]][position][1]      )
	    - 1_0_0_0_0
	)
	return first_solution, distance_of_first_solution






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					Dict:
	__lowerCamelCase    :    Optional[int] 	=			[]

	for n in solution[1:-1]:
		__lowerCamelCase    :    Optional[int] 	=			solution.index(lowerCamelCase__      )
		for kn in solution[1:-1]:
			__lowerCamelCase    :    List[Any] 	=			solution.index(lowerCamelCase__      )
			if n == kn:
				continue

			__lowerCamelCase    :    List[str] 	=			copy.deepcopy(lowerCamelCase__      )
			__lowerCamelCase    :    Dict 	=			kn
			__lowerCamelCase    :    List[Any] 	=			n

			__lowerCamelCase    :    Optional[int] 	=			0

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

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

	__lowerCamelCase    :    str 	=			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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Optional[int]:
	__lowerCamelCase    :    List[Any] 	=			1
	__lowerCamelCase    :    Optional[int] 	=			first_solution
	__lowerCamelCase    :    int 	=			[]
	__lowerCamelCase    :    Tuple 	=			distance_of_first_solution
	__lowerCamelCase    :    Dict 	=			solution

	while count <= iters:
		__lowerCamelCase    :    Any 	=			find_neighborhood(lowerCamelCase__     ,			lowerCamelCase__      )
		__lowerCamelCase    :    str 	=			0
		__lowerCamelCase    :    List[str] 	=			neighborhood[index_of_best_solution]
		__lowerCamelCase    :    str 	=			len(lowerCamelCase__      ) - 1

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

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

		__lowerCamelCase    :    Any 	=			count + 1

	return best_solution_ever, best_cost






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__=None      )  ->					int:
	__lowerCamelCase    :    List[str] 	=			generate_neighbours(args.File      )

	__lowerCamelCase	,			__lowerCamelCase    :    List[str] 	=			generate_first_solution(
	    args.File     ,			lowerCamelCase__      )

	__lowerCamelCase	,			__lowerCamelCase    :    Any 	=			tabu_search(
	    lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			args.Iterations     ,			args.Size     ,			)

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


if __name__ == "__main__":
					a		=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())







import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


a		=argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argument("""--dpm""", action="""store_true""", help="""Enable DPMSolver or not""")
parser.add_argument("""--steps""", default=None, type=int, help="""Num inference steps""")
a		=parser.parse_args()


a		="""cpu"""
a		="""a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""

a		="""path-to-your-trained-model"""
a		=StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
					a		=DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
a		=pipe.to(device)

# to channels last
a		=pipe.unet.to(memory_format=torch.channels_last)
a		=pipe.vae.to(memory_format=torch.channels_last)
a		=pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
					a		=pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
a		=torch.randn(2, 4, 64, 64)
a		=torch.rand(1) * 999
a		=torch.randn(2, 77, 768)
a		=(sample, timestep, encoder_hidden_status)
try:
					a		=ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
					a		=ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
a		=ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
a		=ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
					a		=ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
a		=666
a		=torch.Generator(device).manual_seed(seed)
a		={"""generator""": generator}
if args.steps is not None:
					a		=args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
					a		=pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("""generated.png""")




