






"""simple docstring"""






import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand



def 			__lowerCamelCase       (       lowerCAmelCase__					):
				return EnvironmentCommand()



def 			__lowerCamelCase       (       lowerCAmelCase__					):
				return EnvironmentCommand(args.accelerate_config_file					)







class    snake_case_							(     _lowerCamelCase							):



	"""simple docstring"""

	@staticmethod
	def    _UpperCAmelCase    (   __a    ):


					"""simple docstring"""

					A__							=							parser.add_parser('env'    )
					download_parser.set_defaults(func=A__    )
					download_parser.add_argument(
					    '--accelerate-config_file'	,       default=A__	,       help='The accelerate config file to use for the default values in the launching script.'	,       )
					download_parser.set_defaults(func=A__    )


	def __init__(   self	,       __a	,       *__a    ):


					"""simple docstring"""

					A__							=							accelerate_config_file


	def    _UpperCAmelCase    (   self    ):


					"""simple docstring"""

					A__							=							"""not installed"""
					if is_safetensors_available():
									import safetensors

									A__							=							safetensors.__version__
					elif importlib.util.find_spec('safetensors'    ) is not None:
									import safetensors

									A__							=							f'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

					A__							=							"""not installed"""
					A__							=							"""not found"""
					if is_accelerate_available():
									import accelerate
									from accelerate.commands.config import default_config_file, load_config_from_file

									A__							=							accelerate.__version__
									# Get the default from the config file.
									if self._accelerate_config_file is not None or os.path.isfile(A__    ):
													A__							=							load_config_from_file(self._accelerate_config_file    ).to_dict()

									A__							=							(
									    """\n""".join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]    )
									    if isinstance(A__	,       A__    )
									    else f'''\t{accelerate_config}'''
									)

					A__							=							"""not installed"""
					A__							=							"""NA"""
					if is_torch_available():
									import torch

									A__							=							torch.__version__
									A__							=							torch.cuda.is_available()

					A__							=							"""not installed"""
					A__							=							"""NA"""
					if is_tf_available():
									import tensorflow as tf

									A__							=							tf.__version__
									try:
													# deprecated in v2.1
													A__							=							tf.test.is_gpu_available()
									except AttributeError:
													# returns list of devices, convert to bool
													A__							=							bool(tf.config.list_physical_devices('GPU'    )    )

					A__							=							"""not installed"""
					A__							=							"""not installed"""
					A__							=							"""not installed"""
					A__							=							"""NA"""
					if is_flax_available():
									import flax
									import jax
									import jaxlib

									A__							=							flax.__version__
									A__							=							jax.__version__
									A__							=							jaxlib.__version__
									A__							=							jax.lib.xla_bridge.get_backend().platform

					A__							=							{
					    """`transformers` version""": version,
					    """Platform""": platform.platform(),
					    """Python version""": platform.python_version(),
					    """Huggingface_hub version""": huggingface_hub.__version__,
					    """Safetensors version""": f'''{safetensors_version}''',
					    """Accelerate version""": f'''{accelerate_version}''',
					    """Accelerate config""": f'''{accelerate_config_str}''',
					    """PyTorch version (GPU?)""": f'''{pt_version} ({pt_cuda_available})''',
					    """Tensorflow version (GPU?)""": f'''{tf_version} ({tf_cuda_available})''',
					    """Flax version (CPU?/GPU?/TPU?)""": f'''{flax_version} ({jax_backend})''',
					    """Jax version""": f'''{jax_version}''',
					    """JaxLib version""": f'''{jaxlib_version}''',
					    """Using GPU in script?""": """<fill in>""",
					    """Using distributed or parallel set-up in script?""": """<fill in>""",
					}

					print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'    )
					print(self.format_dict(A__    )    )

					return info






	@staticmethod
	def    _UpperCAmelCase    (   __a    ):


					"""simple docstring"""

					return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()]    ) + "\n"





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())



