

import argparse

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


A_			:					int    			=     16
A_			:					List[str]    			=     32

def 		UpperCamelCase   (lowercase_:					Accelerator       , lowercase_:					int = 16   )	-> Union[str, Any]:
	A__				:       List[str]	  =						AutoTokenizer.from_pretrained("""bert-base-cased"""   )
	A__				:       List[Any]	  =						load_dataset("""glue"""       , """mrpc"""   )

	def tokenize_function(lowercase_:					List[str]   ):
		# max_length=None => use the model max length (it's actually the default)
		A__				:       List[Any]	  =						tokenizer(examples["""sentence1"""]       , examples["""sentence2"""]       , truncation=lowercase_       , max_length=lowercase_   )
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	# starting with the main process first:
	with accelerator.main_process_first():
		A__				:       Union[str, Any]	  =						datasets.map(
		    lowercase_       , batched=lowercase_       , remove_columns=["""idx""", """sentence1""", """sentence2"""]       , )

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	A__				:       Tuple	  =						tokenized_datasets.rename_column("""label"""       , """labels"""   )

	def collate_fn(lowercase_:					List[Any]   ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		A__				:       List[Any]	  =						128 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			A__				:       List[Any]	  =						16
		elif accelerator.mixed_precision != "no":
			A__				:       Tuple	  =						8
		else:
			A__				:       str	  =						None

		return tokenizer.pad(
		    lowercase_       , padding="""longest"""       , max_length=lowercase_       , pad_to_multiple_of=lowercase_       , return_tensors="""pt"""       , )

	# Instantiate dataloaders.
	A__				:       Optional[int]	  =						DataLoader(
	    tokenized_datasets["""train"""]       , shuffle=lowercase_       , collate_fn=lowercase_       , batch_size=lowercase_       , drop_last=lowercase_   )
	A__				:       str	  =						DataLoader(
	    tokenized_datasets["""validation"""]       , shuffle=lowercase_       , collate_fn=lowercase_       , batch_size=lowercase_       , drop_last=(accelerator.mixed_precision == """fp8""")       , )

	return train_dataloader, eval_dataloader

def 		UpperCamelCase   (lowercase_:					List[Any]       , lowercase_:					Any   )	-> Optional[Any]:
	# Initialize accelerator
	A__				:       Tuple	  =						Accelerator(cpu=args.cpu       , mixed_precision=args.mixed_precision   )
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	A__				:       Union[str, Any]	  =						config["""lr"""]
	A__				:       Tuple	  =						int(config["""num_epochs"""]   )
	A__				:       Optional[Any]	  =						int(config["""seed"""]   )
	A__				:       Optional[int]	  =						int(config["""batch_size"""]   )

	A__				:       Optional[Any]	  =						evaluate.load("""glue"""       , """mrpc"""   )

	# If the batch size is too big we use gradient accumulation
	A__				:       List[Any]	  =						1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		A__				:       int	  =						batch_size // MAX_GPU_BATCH_SIZE
		A__				:       Union[str, Any]	  =						MAX_GPU_BATCH_SIZE

	set_seed(lowercase_   )
	A__  ,  A__				:       Tuple	  =						get_dataloaders(lowercase_       , lowercase_   )
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	A__				:       Optional[Any]	  =						AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""       , return_dict=lowercase_   )

	# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
	# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
	# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
	A__				:       str	  =						model.to(accelerator.device   )
	# Instantiate optimizer
	A__				:       Optional[Any]	  =						AdamW(params=model.parameters()       , lr=lowercase_   )

	# Instantiate scheduler
	A__				:       Optional[int]	  =						get_linear_schedule_with_warmup(
	    optimizer=lowercase_       , num_warmup_steps=100       , num_training_steps=(len(lowercase_   ) * num_epochs) // gradient_accumulation_steps       , )

	# Prepare everything
	# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
	# prepare method.

	A__  ,  A__  ,  A__  ,  A__  ,  A__				:       Tuple	  =						accelerator.prepare(
	    lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_   )

	# Now we train the model
	for epoch in range(lowercase_   ):
		model.train()
		for step, batch in enumerate(lowercase_   ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device   )
			A__				:       Dict	  =						model(**lowercase_   )
			A__				:       str	  =						outputs.loss
			A__				:       Dict	  =						loss / gradient_accumulation_steps
			accelerator.backward(lowercase_   )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		for step, batch in enumerate(lowercase_   ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device   )
			with torch.no_grad():
				A__				:       Optional[int]	  =						model(**lowercase_   )
			A__				:       int	  =						outputs.logits.argmax(dim=-1   )
			A__  ,  A__				:       Tuple	  =						accelerator.gather_for_metrics((predictions, batch["""labels"""])   )
			metric.add_batch(
			    predictions=lowercase_       , references=lowercase_       , )

		A__				:       str	  =						metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:"""       , lowercase_   )

def 		UpperCamelCase   ()	-> Tuple:
	A__				:       Union[str, Any]	  =						argparse.ArgumentParser(description="""Simple example of training script."""   )
	parser.add_argument(
	    """--mixed_precision"""       , type=lowercase_       , default=lowercase_       , choices=["""no""", """fp16""", """bf16""", """fp8"""]       , help="""Whether to use mixed precision. Choose"""
	    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
	    """and an Nvidia Ampere GPU."""       , )
	parser.add_argument("""--cpu"""       , action="""store_true"""       , help="""If passed, will train on the CPU."""   )
	A__				:       int	  =						parser.parse_args()
	A__				:       Union[str, Any]	  =						{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(lowercase_       , lowercase_   )


if __name__ == "__main__":
	main()






import argparse
import copy

def 		UpperCamelCase   (lowercase_:					Any   )	-> Union[str, Any]:
	A__				:       int	  =						{}

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

	return dict_of_neighbours

def 		UpperCamelCase   (lowercase_:					Tuple       , lowercase_:					List[str]   )	-> Tuple:

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

	A__				:       List[Any]	  =						[]

	A__				:       str	  =						start_node

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

		first_solution.append(lowercase_   )
		A__				:       Optional[int]	  =						distance_of_first_solution + int(lowercase_   )
		A__				:       str	  =						best_node

	first_solution.append(lowercase_   )

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

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

def 		UpperCamelCase   (lowercase_:					Any       , lowercase_:					str   )	-> Any:
	A__				:       int	  =						[]

	for n in solution[1:-1]:
		A__				:       Union[str, Any]	  =						solution.index(lowercase_   )
		for kn in solution[1:-1]:
			A__				:       List[Any]	  =						solution.index(lowercase_   )
			if n == kn:
				continue

			A__				:       Union[str, Any]	  =						copy.deepcopy(lowercase_   )
			A__				:       int	  =						kn
			A__				:       Optional[int]	  =						n

			A__				:       Dict	  =						0

			for k in _tmp[:-1]:
				A__				:       Dict	  =						_tmp[_tmp.index(lowercase_   ) + 1]
				for i in dict_of_neighbours[k]:
					if i[0] == next_node:
						A__				:       List[Any]	  =						distance + int(i[1]   )
			_tmp.append(lowercase_   )

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

	A__				:       Optional[Any]	  =						len(neighborhood_of_solution[0]   ) - 1

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

def 		UpperCamelCase   (lowercase_:					List[str]       , lowercase_:					Tuple       , lowercase_:					Dict       , lowercase_:					Dict       , lowercase_:					Tuple   )	-> Union[str, Any]:
	A__				:       Union[str, Any]	  =						1
	A__				:       List[str]	  =						first_solution
	A__				:       Dict	  =						[]
	A__				:       Union[str, Any]	  =						distance_of_first_solution
	A__				:       Any	  =						solution

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

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

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

		A__				:       Any	  =						count + 1

	return best_solution_ever, best_cost

def 		UpperCamelCase   (lowercase_:					Union[str, Any]=None   )	-> List[str]:
	A__				:       Union[str, Any]	  =						generate_neighbours(args.File   )

	A__  ,  A__				:       int	  =						generate_first_solution(
	    args.File       , lowercase_   )

	A__  ,  A__				:       str	  =						tabu_search(
	    lowercase_       , lowercase_       , lowercase_       , args.Iterations       , args.Size       , )

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


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



