





'''simple docstring'''







import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

_lowercase       						=  logging.getLogger()

def    __UpperCamelCase	(	) ->List[Any]:
 snake_case				=			argparse.ArgumentParser()
 parser.add_argument('''-f'''     )
 snake_case				=			parser.parse_args()
 return args.f

def    __UpperCamelCase	(	a    : Any     ) ->Optional[Any]:
 snake_case				=			{}
 snake_case				=			os.path.join(__UpperCamelCase					,   '''all_results.json'''     )
 if os.path.exists(__UpperCamelCase     ):
  with open(__UpperCamelCase					,   '''r'''     ) as f:
   snake_case				=			json.load(__UpperCamelCase     )
 else:
  raise ValueError(f"""can't find {path}"""     )
 return results

def    __UpperCamelCase	(	) ->Dict:
 snake_case				=			torch.cuda.is_available() and torch_device == '''cuda'''
 return is_using_cuda and is_apex_available()


_lowercase       						=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 			_lowercase						(       SCREAMING_SNAKE_CASE_					):







     @classmethod
     def     UpperCamelCase			( cls    ) ->	List[str]:
      # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
      snake_case				=			tempfile.mkdtemp()
      snake_case				=			os.path.join(cls.tmpdir    ,   '''default_config.yml'''    )
      write_basic_config(save_location=cls.configPath    )
      snake_case				=			['''accelerate''', '''launch''', '''--config_file''', cls.configPath]







     @classmethod
     def     UpperCamelCase			( cls    ) ->	List[Any]:
      shutil.rmtree(cls.tmpdir    )







     @mock.patch.dict(os.environ    ,   {'''WANDB_MODE''': '''offline'''}    )
     def     UpperCamelCase			( self    ) ->	List[str]:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        """.split()

      if is_cuda_and_apex_available():
       testargs.append('''--fp16'''    )

      run_command(self._launch_args + testargs    )
      snake_case				=			get_results(UpperCamelCase__    )
      self.assertGreaterEqual(result['''eval_accuracy''']    ,   0.7_5    )
      self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,   '''epoch_0'''    )    )    )
      self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,   '''glue_no_trainer'''    )    )    )







     @mock.patch.dict(os.environ    ,   {'''WANDB_MODE''': '''offline'''}    )
     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        """.split()

      if torch.cuda.device_count() > 1:
       # Skipping because there are not enough batches to train the model + would need a drop_last to work.
       return

      run_command(self._launch_args + testargs    )
      snake_case				=			get_results(UpperCamelCase__    )
      self.assertLess(result['''perplexity''']    ,   1_00    )
      self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,   '''epoch_0'''    )    )    )
      self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,   '''clm_no_trainer'''    )    )    )







     @mock.patch.dict(os.environ    ,   {'''WANDB_MODE''': '''offline'''}    )
     def     UpperCamelCase			( self    ) ->	int:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        """.split()

      run_command(self._launch_args + testargs    )
      snake_case				=			get_results(UpperCamelCase__    )
      self.assertLess(result['''perplexity''']    ,   42    )
      self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,   '''epoch_0'''    )    )    )
      self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,   '''mlm_no_trainer'''    )    )    )







     @mock.patch.dict(os.environ    ,   {'''WANDB_MODE''': '''offline'''}    )
     def     UpperCamelCase			( self    ) ->	Optional[int]:
      # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
      snake_case				=			7 if get_gpu_count() > 1 else 2

      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n            --checkpointing_steps epoch\n            --with_tracking\n        """.split()

      run_command(self._launch_args + testargs    )
      snake_case				=			get_results(UpperCamelCase__    )
      self.assertGreaterEqual(result['''eval_accuracy''']    ,   0.7_5    )
      self.assertLess(result['''train_loss''']    ,   0.5    )
      self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,   '''epoch_0'''    )    )    )
      self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,   '''ner_no_trainer'''    )    )    )







     @unittest.skip(reason='''Fix me @muellerzr'''    )
     @mock.patch.dict(os.environ    ,   {'''WANDB_MODE''': '''offline'''}    )
     def     UpperCamelCase			( self    ) ->	List[Any]:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        """.split()

      run_command(self._launch_args + testargs    )
      snake_case				=			get_results(UpperCamelCase__    )
      # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
      self.assertGreaterEqual(result['''eval_f1''']    ,   28    )
      self.assertGreaterEqual(result['''eval_exact''']    ,   28    )
      self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,   '''epoch_0'''    )    )    )
      self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,   '''qa_no_trainer'''    )    )    )







     @mock.patch.dict(os.environ    ,   {'''WANDB_MODE''': '''offline'''}    )
     def     UpperCamelCase			( self    ) ->	Optional[int]:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        """.split()

      run_command(self._launch_args + testargs    )
      snake_case				=			get_results(UpperCamelCase__    )
      self.assertGreaterEqual(result['''eval_accuracy''']    ,   0.8    )
      self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,   '''swag_no_trainer'''    )    )    )







     @slow
     @mock.patch.dict(os.environ    ,   {'''WANDB_MODE''': '''offline'''}    )
     def     UpperCamelCase			( self    ) ->	Any:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        """.split()

      run_command(self._launch_args + testargs    )
      snake_case				=			get_results(UpperCamelCase__    )
      self.assertGreaterEqual(result['''eval_rouge1''']    ,   10    )
      self.assertGreaterEqual(result['''eval_rouge2''']    ,   2    )
      self.assertGreaterEqual(result['''eval_rougeL''']    ,   7    )
      self.assertGreaterEqual(result['''eval_rougeLsum''']    ,   7    )
      self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,   '''epoch_0'''    )    )    )
      self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,   '''summarization_no_trainer'''    )    )    )







     @slow
     @mock.patch.dict(os.environ    ,   {'''WANDB_MODE''': '''offline'''}    )
     def     UpperCamelCase			( self    ) ->	Tuple:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        """.split()

      run_command(self._launch_args + testargs    )
      snake_case				=			get_results(UpperCamelCase__    )
      self.assertGreaterEqual(result['''eval_bleu''']    ,   30    )
      self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,   '''epoch_0'''    )    )    )
      self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,   '''translation_no_trainer'''    )    )    )







     @slow
     def     UpperCamelCase			( self    ) ->	List[str]:
      snake_case				=			logging.StreamHandler(sys.stdout    )
      logger.addHandler(UpperCamelCase__    )

      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        """.split()

      run_command(self._launch_args + testargs    )
      snake_case				=			get_results(UpperCamelCase__    )
      self.assertGreaterEqual(result['''eval_overall_accuracy''']    ,   0.1_0    )







     @mock.patch.dict(os.environ    ,   {'''WANDB_MODE''': '''offline'''}    )
     def     UpperCamelCase			( self    ) ->	List[Any]:
      snake_case				=			self.get_auto_remove_tmp_dir()
      snake_case				=			F"""\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        """.split()

      if is_cuda_and_apex_available():
       testargs.append('''--fp16'''    )

      run_command(self._launch_args + testargs    )
      snake_case				=			get_results(UpperCamelCase__    )
      # The base model scores a 25%
      self.assertGreaterEqual(result['''eval_accuracy''']    ,   0.6    )
      self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,   '''step_1'''    )    )    )
      self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__    ,   '''image_classification_no_trainer'''    )    )    )








'''simple docstring'''







import argparse
import copy

def    __UpperCamelCase	(	a    : Union[str, Any]     ) ->Tuple:
 snake_case				=			{}

 with open(a     ) as f:
  for line in f:
   if line.split()[0] not in dict_of_neighbours:
    snake_case				=			[]
    _list.append([line.split()[1], line.split()[2]]     )
    snake_case				=			_list
   else:
    dict_of_neighbours[line.split()[0]].append(
        [line.split()[1], line.split()[2]]     )
   if line.split()[1] not in dict_of_neighbours:
    snake_case				=			[]
    _list.append([line.split()[0], line.split()[2]]     )
    snake_case				=			_list
   else:
    dict_of_neighbours[line.split()[1]].append(
        [line.split()[0], line.split()[2]]     )

 return dict_of_neighbours

def    __UpperCamelCase	(	a    : Dict					,   a    : Tuple     ) ->int:

 with open(a     ) as f:
  snake_case				=			f.read(1     )
 snake_case				=			start_node

 snake_case				=			[]

 snake_case				=			start_node

 snake_case				=			0
 while visiting not in first_solution:
  snake_case				=			1_0000
  for k in dict_of_neighbours[visiting]:
   if int(k[1]     ) < int(a     ) and k[0] not in first_solution:
    snake_case				=			k[1]
    snake_case				=			k[0]

  first_solution.append(a     )
  snake_case				=			distance_of_first_solution + int(a     )
  snake_case				=			best_node

 first_solution.append(a     )

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

 snake_case				=			(
     distance_of_first_solution
     + int(dict_of_neighbours[first_solution[-2]][position][1]     )
     - 1_0000
 )
 return first_solution, distance_of_first_solution

def    __UpperCamelCase	(	a    : Optional[int]					,   a    : str     ) ->str:
 snake_case				=			[]

 for n in solution[1:-1]:
  snake_case				=			solution.index(a     )
  for kn in solution[1:-1]:
   snake_case				=			solution.index(a     )
   if n == kn:
    continue

   snake_case				=			copy.deepcopy(a     )
   snake_case				=			kn
   snake_case				=			n

   snake_case				=			0

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

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

 snake_case				=			len(neighborhood_of_solution[0]     ) - 1

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

def    __UpperCamelCase	(	a    : Any					,   a    : Optional[Any]					,   a    : int					,   a    : Optional[int]					,   a    : Union[str, Any]     ) ->List[Any]:
 snake_case				=			1
 snake_case				=			first_solution
 snake_case				=			[]
 snake_case				=			distance_of_first_solution
 snake_case				=			solution

 while count <= iters:
  snake_case				=			find_neighborhood(a					,   a     )
  snake_case				=			0
  snake_case				=			neighborhood[index_of_best_solution]
  snake_case				=			len(a     ) - 1

  snake_case				=			False
  while not found:
   snake_case				=			0
   while i < len(a     ):
    if best_solution[i] != solution[i]:
     snake_case				=			best_solution[i]
     snake_case				=			solution[i]
     break
    snake_case				=			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]     )
    snake_case				=			True
    snake_case				=			best_solution[:-1]
    snake_case				=			neighborhood[index_of_best_solution][best_cost_index]
    if cost < best_cost:
     snake_case				=			cost
     snake_case				=			solution
   else:
    snake_case				=			index_of_best_solution + 1
    snake_case				=			neighborhood[index_of_best_solution]

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

  snake_case				=			count + 1

 return best_solution_ever, best_cost

def    __UpperCamelCase	(	a    : Union[str, Any]=None     ) ->Optional[Any]:
 snake_case				=			generate_neighbours(args.File     )

 snake_case				,		snake_case				=			generate_first_solution(
     args.File					,   a     )

 snake_case				,		snake_case				=			tabu_search(
     a					,   a					,   a					,   args.Iterations					,   args.Size					,   )

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


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

