






"""simple docstring"""






import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)





class    snake_case_							(     nn.Module							):



	"""simple docstring"""

	def __init__(   self    ):


					"""simple docstring"""

					super().__init__()
					A__							=							nn.Linear(3	,       4    )
					A__							=							nn.BatchNormad(4    )
					A__							=							nn.Linear(4	,       5    )






	def    _UpperCAmelCase    (   self	,       __a    ):


					"""simple docstring"""

					return self.lineara(self.batchnorm(self.lineara(A__    )    )    )





class    snake_case_							(     unittest.TestCase							):



	"""simple docstring"""

	def    _UpperCAmelCase    (   self    ):


					"""simple docstring"""

					A__							=							ModelForTest()
					with TemporaryDirectory() as tmp_dir:
									offload_state_dict(A__	,       model.state_dict()    )
									A__							=							os.path.join(A__	,       'index.json'    )
									self.assertTrue(os.path.isfile(A__    )    )
									# TODO: add tests on what is inside the index

									for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
													A__							=							os.path.join(A__	,       f'''{key}.dat'''    )
													self.assertTrue(os.path.isfile(A__    )    )
													# TODO: add tests on the fact weights are properly loaded


	def    _UpperCAmelCase    (   self    ):


					"""simple docstring"""

					A__							=							[torch.floataa, torch.floataa, torch.bfloataa]

					for dtype in dtypes:
									A__							=							torch.randn(2	,       3	,       dtype=A__    )
									with TemporaryDirectory() as tmp_dir:
													A__							=							offload_weight(A__	,       'weight'	,       A__	,       {}    )
													A__							=							os.path.join(A__	,       'weight.dat'    )
													self.assertTrue(os.path.isfile(A__    )    )
													self.assertDictEqual(A__	,       {'weight': {'shape': [2, 3], 'dtype': str(A__    ).split('.'    )[1]}}    )

													A__							=							load_offloaded_weight(A__	,       index['weight']    )
													self.assertTrue(torch.equal(A__	,       A__    )    )


	def    _UpperCAmelCase    (   self    ):


					"""simple docstring"""

					A__							=							ModelForTest()
					A__							=							model.state_dict()
					A__							=							{k: v for k, v in state_dict.items() if """linear2""" not in k}
					A__							=							{k: v for k, v in state_dict.items() if """linear2""" in k}

					with TemporaryDirectory() as tmp_dir:
									offload_state_dict(A__	,       A__    )
									A__							=							OffloadedWeightsLoader(state_dict=A__	,       save_folder=A__    )

									# Every key is there with the right value
									self.assertEqual(sorted(A__    )	,       sorted(state_dict.keys()    )    )
									for key, param in state_dict.items():
													self.assertTrue(torch.allclose(A__	,       weight_map[key]    )    )

					A__							=							{k: v for k, v in state_dict.items() if """weight""" in k}
					A__							=							{k: v for k, v in state_dict.items() if """weight""" not in k}

					with TemporaryDirectory() as tmp_dir:
									offload_state_dict(A__	,       A__    )
									A__							=							OffloadedWeightsLoader(state_dict=A__	,       save_folder=A__    )

									# Every key is there with the right value
									self.assertEqual(sorted(A__    )	,       sorted(state_dict.keys()    )    )
									for key, param in state_dict.items():
													self.assertTrue(torch.allclose(A__	,       weight_map[key]    )    )

					with TemporaryDirectory() as tmp_dir:
									offload_state_dict(A__	,       A__    )
									# Duplicates are removed
									A__							=							OffloadedWeightsLoader(state_dict=A__	,       save_folder=A__    )

									# Every key is there with the right value
									self.assertEqual(sorted(A__    )	,       sorted(state_dict.keys()    )    )
									for key, param in state_dict.items():
													self.assertTrue(torch.allclose(A__	,       weight_map[key]    )    )






	def    _UpperCAmelCase    (   self    ):


					"""simple docstring"""

					A__							=							{"""a.1""": 0, """a.10""": 1, """a.2""": 2}
					A__							=							extract_submodules_state_dict(A__	,       ['a.1', 'a.2']    )
					self.assertDictEqual(A__	,       {'a.1': 0, 'a.2': 2}    )

					A__							=							{"""a.1.a""": 0, """a.10.a""": 1, """a.2.a""": 2}
					A__							=							extract_submodules_state_dict(A__	,       ['a.1', 'a.2']    )
					self.assertDictEqual(A__	,       {'a.1.a': 0, 'a.2.a': 2}    )





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())



