
'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel






@require_tf
class  lowerCAmelCase__   :
					lowerCAmelCase_        =     MBartConfig
					lowerCAmelCase_        =     {}
					lowerCAmelCase_        =     '''gelu'''






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=13				,							__SCREAMING_SNAKE_CASE=7				,							__SCREAMING_SNAKE_CASE=True				,							__SCREAMING_SNAKE_CASE=False				,							__SCREAMING_SNAKE_CASE=99				,							__SCREAMING_SNAKE_CASE=32				,							__SCREAMING_SNAKE_CASE=2				,							__SCREAMING_SNAKE_CASE=4				,							__SCREAMING_SNAKE_CASE=37				,							__SCREAMING_SNAKE_CASE=0.1				,							__SCREAMING_SNAKE_CASE=0.1				,							__SCREAMING_SNAKE_CASE=20				,							__SCREAMING_SNAKE_CASE=2				,							__SCREAMING_SNAKE_CASE=1				,							__SCREAMING_SNAKE_CASE=0				,							):


											"""simple docstring"""





											lowercase_	:     List[str]													=       parent
											lowercase_	:     Optional[int]													=       batch_size
											lowercase_	:     Optional[int]													=       seq_length
											lowercase_	:     List[str]													=       is_training
											lowercase_	:     Dict													=       use_labels
											lowercase_	:     Optional[Any]													=       vocab_size
											lowercase_	:     Tuple													=       hidden_size
											lowercase_	:     Dict													=       num_hidden_layers
											lowercase_	:     int													=       num_attention_heads
											lowercase_	:     Union[str, Any]													=       intermediate_size
											lowercase_	:     int													=       hidden_dropout_prob
											lowercase_	:     Optional[int]													=       attention_probs_dropout_prob
											lowercase_	:     int													=       max_position_embeddings
											lowercase_	:     Union[str, Any]													=       eos_token_id
											lowercase_	:     str													=       pad_token_id
											lowercase_	:     Tuple													=       bos_token_id






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Union[str, Any]													=       ids_tensor([self.batch_size, self.seq_length - 1]				,							self.vocab_size		)
											lowercase_	:     Any													=       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)				,							1		)
											lowercase_	:     List[str]													=       tf.concat([input_ids, eos_tensor]				,							axis=1		)

											lowercase_	:     Dict													=       ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size		)

											lowercase_	:     Any													=       self.config_cls(
											    vocab_size=self.vocab_size				,							d_model=self.hidden_size				,							encoder_layers=self.num_hidden_layers				,							decoder_layers=self.num_hidden_layers				,							encoder_attention_heads=self.num_attention_heads				,							decoder_attention_heads=self.num_attention_heads				,							encoder_ffn_dim=self.intermediate_size				,							decoder_ffn_dim=self.intermediate_size				,							dropout=self.hidden_dropout_prob				,							attention_dropout=self.attention_probs_dropout_prob				,							max_position_embeddings=self.max_position_embeddings				,							eos_token_ids=[2]				,							bos_token_id=self.bos_token_id				,							pad_token_id=self.pad_token_id				,							decoder_start_token_id=self.pad_token_id				,							**self.config_updates				,							)
											lowercase_	:     str													=       prepare_mbart_inputs_dict(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
											return config, inputs_dict






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       TFMBartModel(config=__SCREAMING_SNAKE_CASE		).get_decoder()
											lowercase_	:     Dict													=       inputs_dict['''input_ids''']

											lowercase_	:     Tuple													=       input_ids[:1, :]
											lowercase_	:     str													=       inputs_dict['''attention_mask'''][:1, :]
											lowercase_	:     Optional[int]													=       inputs_dict['''head_mask''']
											lowercase_	:     Union[str, Any]													=       1

											# first forward pass
											lowercase_	:     str													=       model(__SCREAMING_SNAKE_CASE				,							attention_mask=__SCREAMING_SNAKE_CASE				,							head_mask=__SCREAMING_SNAKE_CASE				,							use_cache=__SCREAMING_SNAKE_CASE		)

											lowercase_						, lowercase_	:     Optional[int]													=       outputs.to_tuple()
											lowercase_	:     Tuple													=       past_key_values[1]







def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Optional[int]      ,      __SCREAMING_SNAKE_CASE					: Any      ,      __SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: List[Any]=None      ,      __SCREAMING_SNAKE_CASE					: Dict=None      ,      __SCREAMING_SNAKE_CASE					: Union[str, Any]=None      ,      __SCREAMING_SNAKE_CASE					: int=None      ,      __SCREAMING_SNAKE_CASE					: List[str]=None      ,      ):



						"""simple docstring"""





						if attention_mask is None:
												lowercase_	:     Optional[int]													=       tf.cast(tf.math.not_equal(__SCREAMING_SNAKE_CASE      ,      config.pad_token_id							)      ,      tf.inta							)
						if decoder_attention_mask is None:
												lowercase_	:     str													=       tf.concat(
												    [
												        tf.ones(decoder_input_ids[:, :1].shape      ,      dtype=tf.inta							),
												        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]      ,      config.pad_token_id							)      ,      tf.inta							),
												    ]      ,      axis=-1      ,      )
						if head_mask is None:
												lowercase_	:     List[str]													=       tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
						if decoder_head_mask is None:
												lowercase_	:     Any													=       tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
						if cross_attn_head_mask is None:
												lowercase_	:     List[str]													=       tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": decoder_attention_mask,
						    "head_mask": head_mask,
						    "decoder_head_mask": decoder_head_mask,
						    "cross_attn_head_mask": cross_attn_head_mask,
						}






@require_tf
class  lowerCAmelCase__   (  lowerCamelCase_    ,						lowerCamelCase_    ,						unittest.TestCase     ):
					lowerCAmelCase_        =     (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
					lowerCAmelCase_        =     (TFMBartForConditionalGeneration,) if is_tf_available() else ()
					lowerCAmelCase_        =     (
					    {
					        '''conversational''': TFMBartForConditionalGeneration,
					        '''feature-extraction''': TFMBartModel,
					        '''summarization''': TFMBartForConditionalGeneration,
					        '''text2text-generation''': TFMBartForConditionalGeneration,
					        '''translation''': TFMBartForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					lowerCAmelCase_        =     True
					lowerCAmelCase_        =     False
					lowerCAmelCase_        =     False






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
																	# Exception encountered when calling layer '...'
																	return True

											return False






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       TFMBartModelTester(self		)
											lowercase_	:     Union[str, Any]													=       ConfigTester(self				,							config_class=__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											self.config_tester.run_common_tests()






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE		)







@require_sentencepiece
@require_tokenizers
@require_tf
class  lowerCAmelCase__   (  unittest.TestCase     ):
					lowerCAmelCase_        =     [
					    ''' UN Chief Says There Is No Military Solution in Syria''',
					]
					lowerCAmelCase_        =     [
					    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
					]
					lowerCAmelCase_        =     '''facebook/mbart-large-en-ro'''






					@cached_property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return AutoTokenizer.from_pretrained(self.model_name		)






					@cached_property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Any													=       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
											return model






					def 	_snake_case    (   self				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       self.translate_src_text(**__SCREAMING_SNAKE_CASE		)
											self.assertListEqual(self.expected_text				,							__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Union[str, Any]													=       self.tokenizer(self.src_text				,							**__SCREAMING_SNAKE_CASE				,							return_tensors='''tf'''		)
											lowercase_	:     str													=       self.model.generate(
											    model_inputs.input_ids				,							attention_mask=model_inputs.attention_mask				,							num_beams=2		)
											lowercase_	:     List[str]													=       self.tokenizer.batch_decode(__SCREAMING_SNAKE_CASE				,							skip_special_tokens=__SCREAMING_SNAKE_CASE		)
											return generated_words






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											self._assert_generated_batch_equal_expected()





'''simple docstring'''







import argparse
import copy
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Optional[int]							):



						"""simple docstring"""





						lowercase_	:     List[Any]													=       {}

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

						return dict_of_neighbours
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: Tuple							):



						"""simple docstring"""





						with open(__SCREAMING_SNAKE_CASE							) as f:
												lowercase_	:     List[str]													=       f.read(1							)
						lowercase_	:     Optional[int]													=       start_node

						lowercase_	:     Any													=       []

						lowercase_	:     List[str]													=       start_node

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

												first_solution.append(__SCREAMING_SNAKE_CASE							)
												lowercase_	:     Optional[Any]													=       distance_of_first_solution + int(__SCREAMING_SNAKE_CASE							)
												lowercase_	:     int													=       best_node

						first_solution.append(__SCREAMING_SNAKE_CASE							)

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

						lowercase_	:     Optional[Any]													=       (
						    distance_of_first_solution
						    + int(dict_of_neighbours[first_solution[-2]][position][1]							)
						    - 10000
						)
						return first_solution, distance_of_first_solution
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Optional[Any]      ,      __SCREAMING_SNAKE_CASE					: Union[str, Any]							):



						"""simple docstring"""





						lowercase_	:     Tuple													=       []

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

																		lowercase_	:     Dict													=       copy.deepcopy(__SCREAMING_SNAKE_CASE							)
																		lowercase_	:     Dict													=       kn
																		lowercase_	:     List[Any]													=       n

																		lowercase_	:     str													=       0

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

																		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 __SCREAMING_SNAKE_CASE							: x[index_of_last_item_in_the_list]							)
						return neighborhood_of_solution
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Tuple      ,      __SCREAMING_SNAKE_CASE					: List[Any]      ,      __SCREAMING_SNAKE_CASE					: Optional[int]      ,      __SCREAMING_SNAKE_CASE					: Optional[int]      ,      __SCREAMING_SNAKE_CASE					: Optional[Any]							):



						"""simple docstring"""





						lowercase_	:     Optional[int]													=       1
						lowercase_	:     List[str]													=       first_solution
						lowercase_	:     Dict													=       []
						lowercase_	:     List[str]													=       distance_of_first_solution
						lowercase_	:     Optional[Any]													=       solution

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

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

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

												lowercase_	:     List[Any]													=       count + 1

						return best_solution_ever, best_cost
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[str]=None							):



						"""simple docstring"""





						lowercase_	:     Any													=       generate_neighbours(args.File							)

						lowercase_						, lowercase_	:     Union[str, Any]													=       generate_first_solution(
						    args.File      ,      __SCREAMING_SNAKE_CASE							)

						lowercase_						, lowercase_	:     Optional[int]													=       tabu_search(
						    __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      args.Iterations      ,      args.Size      ,      )

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


if __name__ == "__main__":
							_lowercase :  Any    							=       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())



