

'''simple docstring'''
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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







def 					_A		(	snake_case     )			->	List[Any]:
   _lowercase			:      List[Any]			     =   "huggingface/label-files"
   _lowercase			:      int			     =   "imagenet-1k-id2label.json"
   _lowercase			:      Tuple			     =   json.load(open(hf_hub_download(_lowerCamelCase		,							_lowerCamelCase		,							repo_type="dataset"     )		,							"r"     )     )
   _lowercase			:      Tuple			     =   {int(_lowerCamelCase     ): v for k, v in idalabel.items()}
   _lowercase			:      Union[str, Any]			     =   {v: k for k, v in idalabel.items()}

   _lowercase			:      Tuple			     =   "std_conv" if "bit" in model_name else False

   # note that when using BiT as backbone for ViT-hybrid checkpoints,
   # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
   # config.conv_layer = "std_conv_same"
   _lowercase			:      Optional[int]			     =   BitConfig(
       conv_layer=_lowerCamelCase		,							num_labels=10_00		,							idalabel=_lowerCamelCase		,							labelaid=_lowerCamelCase		,							)

   return config







def 					_A		(	snake_case     )			->	int:




   if "stem.conv" in name:
      _lowercase			:      List[str]			     =   name.replace("stem.conv"		,							"bit.embedder.convolution"     )
   if "blocks" in name:
      _lowercase			:      Any			     =   name.replace("blocks"		,							"layers"     )
   if "head.fc" in name:
      _lowercase			:      Optional[Any]			     =   name.replace("head.fc"		,							"classifier.1"     )
   if name.startswith("norm"     ):
      _lowercase			:      Any			     =   "bit." + name
   if "bit" not in name and "classifier" not in name:
      _lowercase			:      Dict			     =   "bit.encoder." + name

   return name







def 					_A		(	)			->	Dict:
   _lowercase			:      Tuple			     =   "http://images.cocodataset.org/val2017/000000039769.jpg"
   _lowercase			:      Optional[int]			     =   Image.open(requests.get(_lowerCamelCase		,							stream=_lowerCamelCase     ).raw     )
   return im







@torch.no_grad()
def 					_A		(	snake_case		,							snake_case		,							snake_case=False     )			->	List[Any]:
   _lowercase			:      Dict			     =   get_config(_lowerCamelCase     )

   # load original model from timm
   _lowercase			:      int			     =   create_model(_lowerCamelCase		,							pretrained=_lowerCamelCase     )
   timm_model.eval()

   # load state_dict of original model
   _lowercase			:      Any			     =   timm_model.state_dict()
   for key in state_dict.copy().keys():
      _lowercase			:      Dict			     =   state_dict.pop(_lowerCamelCase     )
      _lowercase			:      Tuple			     =   val.squeeze() if "head" in key else val

   # load HuggingFace model
   _lowercase			:      Optional[Any]			     =   BitForImageClassification(_lowerCamelCase     )
   model.eval()
   model.load_state_dict(_lowerCamelCase     )

   # create image processor
   _lowercase			:      Dict			     =   create_transform(**resolve_data_config({}		,							model=_lowerCamelCase     )     )
   _lowercase			:      Optional[int]			     =   transform.transforms

   _lowercase			:      Tuple			     =   {
       "bilinear": PILImageResampling.BILINEAR,
       "bicubic": PILImageResampling.BICUBIC,
       "nearest": PILImageResampling.NEAREST,
   }

   _lowercase			:      Tuple			     =   BitImageProcessor(
       do_resize=_lowerCamelCase		,							size={"shortest_edge": timm_transforms[0].size}		,							resample=pillow_resamplings[timm_transforms[0].interpolation.value]		,							do_center_crop=_lowerCamelCase		,							crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}		,							do_normalize=_lowerCamelCase		,							image_mean=timm_transforms[-1].mean.tolist()		,							image_std=timm_transforms[-1].std.tolist()		,							)

   _lowercase			:      Optional[int]			     =   prepare_img()
   _lowercase			:      Any			     =   transform(_lowerCamelCase     ).unsqueeze(0     )
   _lowercase			:      Optional[int]			     =   processor(_lowerCamelCase		,							return_tensors="pt"     ).pixel_values

   # verify pixel values
   assert torch.allclose(_lowerCamelCase		,							_lowerCamelCase     )

   # verify logits
   with torch.no_grad():
      _lowercase			:      Tuple			     =   model(_lowerCamelCase     )
      _lowercase			:      str			     =   outputs.logits

   print("Logits:"		,							logits[0, :3]     )
   print("Predicted class:"		,							model.config.idalabel[logits.argmax(-1     ).item()]     )
   _lowercase			:      Union[str, Any]			     =   timm_model(_lowerCamelCase     )
   assert timm_logits.shape == outputs.logits.shape
   assert torch.allclose(_lowerCamelCase		,							outputs.logits		,							atol=1E-3     )
   print("Looks ok!"     )

   if pytorch_dump_folder_path is not None:
      Path(_lowerCamelCase     ).mkdir(exist_ok=_lowerCamelCase     )
      print(F'''Saving model {model_name} and processor to {pytorch_dump_folder_path}'''     )
      model.save_pretrained(_lowerCamelCase     )
      processor.save_pretrained(_lowerCamelCase     )

   if push_to_hub:
      print(F'''Pushing model {model_name} and processor to the hub'''     )
      model.push_to_hub(F'''ybelkada/{model_name}'''     )
      processor.push_to_hub(F'''ybelkada/{model_name}'''     )


if __name__ == "__main__":
    _snake_case     =  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--model_name',
        default='resnetv2_50x1_bitm',
        type=str,
        help='Name of the BiT timm model you\'d like to convert.',
    )
    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 to push the model to the hub.',
    )

    _snake_case     =  parser.parse_args()
    convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











import argparse
import copy






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    {}

							with open(_lowerCamelCase  ) as f:
														for line in f:
																					if line.split()[0] not in dict_of_neighbours:
																												_lowerCAmelCase     :							Tuple    =    []
																												_list.append([line.split()[1], line.split()[2]]  )
																												_lowerCAmelCase     :							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:
																												_lowerCAmelCase     :							str    =    []
																												_list.append([line.split()[0], line.split()[2]]  )
																												_lowerCAmelCase     :							Any    =    _list
																					else:
																												dict_of_neighbours[line.split()[1]].append(
																												    [line.split()[0], line.split()[2]]  )

							return dict_of_neighbours






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							with open(_lowerCamelCase  ) as f:
														_lowerCAmelCase     :							str    =    f.read(1  )
							_lowerCAmelCase     :							str    =    start_node

							_lowerCAmelCase     :							List[str]    =    []

							_lowerCAmelCase     :							Any    =    start_node

							_lowerCAmelCase     :							str    =    0
							while visiting not in first_solution:
														_lowerCAmelCase     :							Dict    =    10_000
														for k in dict_of_neighbours[visiting]:
																					if int(k[1]  ) < int(_lowerCamelCase  ) and k[0] not in first_solution:
																												_lowerCAmelCase     :							List[str]    =    k[1]
																												_lowerCAmelCase     :							List[Any]    =    k[0]

														first_solution.append(_lowerCamelCase  )
														_lowerCAmelCase     :							Optional[int]    =    distance_of_first_solution + int(_lowerCamelCase  )
														_lowerCAmelCase     :							str    =    best_node

							first_solution.append(_lowerCamelCase  )

							_lowerCAmelCase     :							Union[str, Any]    =    0
							for k in dict_of_neighbours[first_solution[-2]]:
														if k[0] == start_node:
																					break
														position += 1

							_lowerCAmelCase     :							Tuple    =    (
							    distance_of_first_solution
							    + int(dict_of_neighbours[first_solution[-2]][position][1]  )
							    - 10_000
							)
							return first_solution, distance_of_first_solution






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Tuple    =    []

							for n in solution[1:-1]:
														_lowerCAmelCase     :							Dict    =    solution.index(_lowerCamelCase  )
														for kn in solution[1:-1]:
																					_lowerCAmelCase     :							Dict    =    solution.index(_lowerCamelCase  )
																					if n == kn:
																												continue

																					_lowerCAmelCase     :							Optional[int]    =    copy.deepcopy(_lowerCamelCase  )
																					_lowerCAmelCase     :							int    =    kn
																					_lowerCAmelCase     :							Dict    =    n

																					_lowerCAmelCase     :							Optional[int]    =    0

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

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

							_lowerCAmelCase     :							List[Any]    =    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				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							List[str]    =    1
							_lowerCAmelCase     :							int    =    first_solution
							_lowerCAmelCase     :							Tuple    =    []
							_lowerCAmelCase     :							Tuple    =    distance_of_first_solution
							_lowerCAmelCase     :							Optional[int]    =    solution

							while count <= iters:
														_lowerCAmelCase     :							int    =    find_neighborhood(_lowerCamelCase				, _lowerCamelCase  )
														_lowerCAmelCase     :							Tuple    =    0
														_lowerCAmelCase     :							Dict    =    neighborhood[index_of_best_solution]
														_lowerCAmelCase     :							int    =    len(_lowerCamelCase  ) - 1

														_lowerCAmelCase     :							Union[str, Any]    =    False
														while not found:
																					_lowerCAmelCase     :							Tuple    =    0
																					while i < len(_lowerCamelCase  ):
																												if best_solution[i] != solution[i]:
																																			_lowerCAmelCase     :							str    =    best_solution[i]
																																			_lowerCAmelCase     :							Tuple    =    solution[i]
																																			break
																												_lowerCAmelCase     :							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]  )
																												_lowerCAmelCase     :							Optional[int]    =    True
																												_lowerCAmelCase     :							Optional[Any]    =    best_solution[:-1]
																												_lowerCAmelCase     :							Tuple    =    neighborhood[index_of_best_solution][best_cost_index]
																												if cost < best_cost:
																																			_lowerCAmelCase     :							Union[str, Any]    =    cost
																																			_lowerCAmelCase     :							List[Any]    =    solution
																					else:
																												_lowerCAmelCase     :							Optional[Any]    =    index_of_best_solution + 1
																												_lowerCAmelCase     :							Optional[Any]    =    neighborhood[index_of_best_solution]

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

														_lowerCAmelCase     :							int    =    count + 1

							return best_solution_ever, best_cost






def 				A				(					_lowerCamelCase=None  ):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    generate_neighbours(args.File  )

							_lowerCAmelCase   ,    _lowerCAmelCase     :							List[str]    =    generate_first_solution(
							    args.File				, _lowerCamelCase  )

							_lowerCAmelCase   ,    _lowerCAmelCase     :							Any    =    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		=  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())
