




import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case										=       logging.get_logger(__name__)

_snake_case										=       [
    ("bert.bert", "visual_bert"),
    ("bert.cls", "cls"),
    ("bert.classifier", "cls"),
    ("token_type_embeddings_visual", "visual_token_type_embeddings"),
    ("position_embeddings_visual", "visual_position_embeddings"),
    ("projection", "visual_projection"),
]

_snake_case										=       [
    "nlvr2_coco_pre_trained.th",
    "nlvr2_fine_tuned.th",
    "nlvr2_pre_trained.th",
    "vcr_coco_pre_train.th",
    "vcr_fine_tune.th",
    "vcr_pre_train.th",
    "vqa_coco_pre_trained.th",
    "vqa_fine_tuned.th",
    "vqa_pre_trained.th",
]







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     int			=						torch.load(snake_case_,map_location="""cpu"""     )
      return sd







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_=rename_keys_prefix     ):
      _A							:     Dict			=						OrderedDict()
      _A							:     Tuple			=						torch.arange(config.max_position_embeddings     ).expand((1, -1)     )
      # detector_d = OrderedDict()
      for key in d:
            if "detector" in key:
                  # detector_d[key.replace('detector.','')] = d[key]
                  continue
            _A							:     Dict			=						key
            for name_pair in rename_keys_prefix:
                  _A							:     Tuple			=						new_key.replace(name_pair[0],name_pair[1]     )
            _A							:     Dict			=						d[key]
            if key == "bert.cls.predictions.decoder.weight":
                  # Old bert code didn't have `decoder.bias`, but was added separately
                  _A							:     Optional[int]			=						new_d["""cls.predictions.bias"""]
      return new_d







@torch.no_grad()
def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      assert (
          checkpoint_path.split("""/"""     )[-1] in ACCEPTABLE_CHECKPOINTS
      ), f'''The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'''

      # Get Config
      if "pre" in checkpoint_path:
            _A							:     str			=						"""pretraining"""
            if "vcr" in checkpoint_path:
                  _A							:     int			=						{"""visual_embedding_dim""": 512}
            elif "vqa_advanced" in checkpoint_path:
                  _A							:     Any			=						{"""visual_embedding_dim""": 2048}
            elif "vqa" in checkpoint_path:
                  _A							:     int			=						{"""visual_embedding_dim""": 2048}
            elif "nlvr" in checkpoint_path:
                  _A							:     Optional[int]			=						{"""visual_embedding_dim""": 1024}
            else:
                  raise NotImplementedError(f'''No implementation found for `{checkpoint_path}`.'''     )
      else:
            if "vcr" in checkpoint_path:
                  _A							:     Optional[Any]			=						{"""visual_embedding_dim""": 512}
                  _A							:     List[Any]			=						"""multichoice"""
            elif "vqa_advanced" in checkpoint_path:
                  _A							:     List[Any]			=						{"""visual_embedding_dim""": 2048}
                  _A							:     str			=						"""vqa_advanced"""
            elif "vqa" in checkpoint_path:
                  _A							:     Optional[Any]			=						{"""visual_embedding_dim""": 2048, """num_labels""": 3129}
                  _A							:     Any			=						"""vqa"""
            elif "nlvr" in checkpoint_path:
                  _A							:     int			=						{
                      """visual_embedding_dim""": 1024,
                      """num_labels""": 2,
                  }
                  _A							:     List[Any]			=						"""nlvr"""

      _A							:     Tuple			=						VisualBertConfig(**snake_case_     )

      # Load State Dict
      _A							:     Any			=						load_state_dict(snake_case_     )

      _A							:     List[Any]			=						get_new_dict(snake_case_,snake_case_     )

      if model_type == "pretraining":
            _A							:     Tuple			=						VisualBertForPreTraining(snake_case_     )
      elif model_type == "vqa":
            _A							:     Tuple			=						VisualBertForQuestionAnswering(snake_case_     )
      elif model_type == "nlvr":
            _A							:     Union[str, Any]			=						VisualBertForVisualReasoning(snake_case_     )
      elif model_type == "multichoice":
            _A							:     List[Any]			=						VisualBertForMultipleChoice(snake_case_     )

      model.load_state_dict(snake_case_     )
      # Save Checkpoints
      Path(snake_case_     ).mkdir(exist_ok=snake_case_     )
      model.save_pretrained(snake_case_     )


if __name__ == "__main__":
  _snake_case										=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument("orig_checkpoint_path", type=str, help="A path to .th on local filesystem.")
  parser.add_argument("pytorch_dump_folder_path", type=str, help="Path to the output PyTorch model.")
  _snake_case										=       parser.parse_args()
  convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)








import argparse
import copy



def _a				(	lowerCamelCase:   List[Any] )	->						List[str]:







      '''simple docstring'''






      __A          =				{}

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

      return dict_of_neighbours



def _a				(	lowerCamelCase:   Any					,				lowerCamelCase:   Optional[Any] )	->						Dict:







      '''simple docstring'''






      with open(lowerCamelCase ) as f:
            __A          =				f.read(1 )
      __A          =				start_node

      __A          =				[]

      __A          =				start_node

      __A          =				0
      while visiting not in first_solution:
            __A          =				1_00_00
            for k in dict_of_neighbours[visiting]:
                  if int(k[1] ) < int(lowerCamelCase ) and k[0] not in first_solution:
                        __A          =				k[1]
                        __A          =				k[0]

            first_solution.append(lowerCamelCase )
            __A          =				distance_of_first_solution + int(lowerCamelCase )
            __A          =				best_node

      first_solution.append(lowerCamelCase )

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

      __A          =				(
          distance_of_first_solution
          + int(dict_of_neighbours[first_solution[-2]][position][1] )
          - 1_00_00
      )
      return first_solution, distance_of_first_solution



def _a				(	lowerCamelCase:   List[str]					,				lowerCamelCase:   Any )	->						Any:







      '''simple docstring'''






      __A          =				[]

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

                  __A          =				copy.deepcopy(lowerCamelCase )
                  __A          =				kn
                  __A          =				n

                  __A          =				0

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

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

      __A          =				len(neighborhood_of_solution[0] ) - 1

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



def _a				(	lowerCamelCase:   Optional[int]					,				lowerCamelCase:   Dict					,				lowerCamelCase:   Any					,				lowerCamelCase:   Optional[int]					,				lowerCamelCase:   Union[str, Any] )	->						Any:







      '''simple docstring'''






      __A          =				1
      __A          =				first_solution
      __A          =				[]
      __A          =				distance_of_first_solution
      __A          =				solution

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

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

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

            __A          =				count + 1

      return best_solution_ever, best_cost



def _a				(	lowerCamelCase:   List[str]=None )	->						str:







      '''simple docstring'''






      __A          =				generate_neighbours(args.File )

      __A   ,						__A          =				generate_first_solution(
          args.File					,				lowerCamelCase )

      __A   ,						__A          =				tabu_search(
          lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				args.Iterations					,				args.Size					,				)

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


if __name__ == "__main__":
       snake_case__					:							Tuple													=     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())





