





import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor



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







      '''simple docstring'''






      __A          =				SwinConfig(image_size=1_92 )

      if "base" in model_name:
            __A          =				6
            __A          =				1_28
            __A          =				(2, 2, 18, 2)
            __A          =				(4, 8, 16, 32)
      elif "large" in model_name:
            __A          =				12
            __A          =				1_92
            __A          =				(2, 2, 18, 2)
            __A          =				(6, 12, 24, 48)
      else:
            raise ValueError('''Model not supported, only supports base and large variants''' )

      __A          =				window_size
      __A          =				embed_dim
      __A          =				depths
      __A          =				num_heads

      return config



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







      '''simple docstring'''






      if "encoder.mask_token" in name:
            __A          =				name.replace('''encoder.mask_token'''					,				'''embeddings.mask_token''' )
      if "encoder.patch_embed.proj" in name:
            __A          =				name.replace('''encoder.patch_embed.proj'''					,				'''embeddings.patch_embeddings.projection''' )
      if "encoder.patch_embed.norm" in name:
            __A          =				name.replace('''encoder.patch_embed.norm'''					,				'''embeddings.norm''' )
      if "attn.proj" in name:
            __A          =				name.replace('''attn.proj'''					,				'''attention.output.dense''' )
      if "attn" in name:
            __A          =				name.replace('''attn'''					,				'''attention.self''' )
      if "norm1" in name:
            __A          =				name.replace('''norm1'''					,				'''layernorm_before''' )
      if "norm2" in name:
            __A          =				name.replace('''norm2'''					,				'''layernorm_after''' )
      if "mlp.fc1" in name:
            __A          =				name.replace('''mlp.fc1'''					,				'''intermediate.dense''' )
      if "mlp.fc2" in name:
            __A          =				name.replace('''mlp.fc2'''					,				'''output.dense''' )

      if name == "encoder.norm.weight":
            __A          =				'''layernorm.weight'''
      if name == "encoder.norm.bias":
            __A          =				'''layernorm.bias'''

      if "decoder" in name:
            pass
      else:
            __A          =				'''swin.''' + name

      return name



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







      '''simple docstring'''






      for key in orig_state_dict.copy().keys():
            __A          =				orig_state_dict.pop(lowerCamelCase )

            if "attn_mask" in key:
                  pass
            elif "qkv" in key:
                  __A          =				key.split('''.''' )
                  __A          =				int(key_split[2] )
                  __A          =				int(key_split[4] )
                  __A          =				model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                  if "weight" in key:
                        __A          =				val[:dim, :]
                        __A          =				val[
                            dim : dim * 2, :
                        ]
                        __A          =				val[-dim:, :]
                  else:
                        __A          =				val[
                            :dim
                        ]
                        __A          =				val[
                            dim : dim * 2
                        ]
                        __A          =				val[
                            -dim:
                        ]
            else:
                  __A          =				val

      return orig_state_dict



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







      '''simple docstring'''






      __A          =				torch.load(lowerCamelCase					,				map_location='''cpu''' )['''model''']

      __A          =				get_swin_config(lowerCamelCase )
      __A          =				SwinForMaskedImageModeling(lowerCamelCase )
      model.eval()

      __A          =				convert_state_dict(lowerCamelCase					,				lowerCamelCase )
      model.load_state_dict(lowerCamelCase )

      __A          =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''

      __A          =				ViTImageProcessor(size={'''height''': 1_92, '''width''': 1_92} )
      __A          =				Image.open(requests.get(lowerCamelCase					,				stream=lowerCamelCase ).raw )
      __A          =				image_processor(images=lowerCamelCase					,				return_tensors='''pt''' )

      with torch.no_grad():
            __A          =				model(**lowerCamelCase ).logits

      print(outputs.keys() )
      print('''Looks ok!''' )

      if pytorch_dump_folder_path is not None:
            print(F"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
            model.save_pretrained(lowerCamelCase )

            print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
            image_processor.save_pretrained(lowerCamelCase )

      if push_to_hub:
            print(F"""Pushing model and image processor for {model_name} to hub""" )
            model.push_to_hub(F"""microsoft/{model_name}""" )
            image_processor.push_to_hub(F"""microsoft/{model_name}""" )


if __name__ == "__main__":
       snake_case__					:							Union[str, Any]													=     argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--model_name',
           default='swin-base-simmim-window6-192',
           type=str,
           choices=['swin-base-simmim-window6-192', 'swin-large-simmim-window12-192'],
           help='Name of the Swin SimMIM model you\'d like to convert.',
       )
       parser.add_argument(
           '--checkpoint_path',
           default='/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth',
           type=str,
           help='Path to the original PyTorch checkpoint (.pth file).',
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
       )
       parser.add_argument(
           '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
       )

       snake_case__					:							int													=     parser.parse_args()
       convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)












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())





