





'''simple docstring'''







import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


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

def    __UpperCamelCase	(	a    : int					,   a    : Tuple=False     ) ->str:
 snake_case				=			[]
 for i in range(config.num_hidden_layers     ):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")     )
  rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")     )
  rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")     )
  rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")     )
  rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")     )
  rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")     )
  rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")     )
  rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")     )
  rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")     )
  rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")     )

 # projection layer + position embeddings
 rename_keys.extend(
     [
         ('''cls_token''', '''vit.embeddings.cls_token'''),
         ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
         ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
         ('''pos_embed''', '''vit.embeddings.position_embeddings'''),
     ]     )

 if base_model:
  # layernorm + pooler
  rename_keys.extend(
      [
          ('''norm.weight''', '''layernorm.weight'''),
          ('''norm.bias''', '''layernorm.bias'''),
          ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
          ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
      ]     )

  # if just the base model, we should remove "vit" from all keys that start with "vit"
  snake_case				=			[(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''     ) else pair for pair in rename_keys]
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ('''norm.weight''', '''vit.layernorm.weight'''),
          ('''norm.bias''', '''vit.layernorm.bias'''),
          ('''head.weight''', '''classifier.weight'''),
          ('''head.bias''', '''classifier.bias'''),
      ]     )

 return rename_keys

def    __UpperCamelCase	(	a    : str					,   a    : Any					,   a    : Optional[Any]=False     ) ->int:
 for i in range(config.num_hidden_layers     ):
  if base_model:
   snake_case				=			''''''
  else:
   snake_case				=			'''vit.'''
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  snake_case				=			state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""     )
  snake_case				=			state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""     )
  # next, add query, keys and values (in that order) to the state dict
  snake_case				=			in_proj_weight[
      : config.hidden_size, :
  ]
  snake_case				=			in_proj_bias[: config.hidden_size]
  snake_case				=			in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  snake_case				=			in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  snake_case				=			in_proj_weight[
      -config.hidden_size :, :
  ]
  snake_case				=			in_proj_bias[-config.hidden_size :]

def    __UpperCamelCase	(	a    : Union[str, Any]     ) ->List[str]:
 snake_case				=			['''head.weight''', '''head.bias''']
 for k in ignore_keys:
  state_dict.pop(a					,   a     )

def    __UpperCamelCase	(	a    : Optional[Any]					,   a    : Tuple					,   a    : Optional[Any]     ) ->Optional[int]:
 snake_case				=			dct.pop(a     )
 snake_case				=			val

def    __UpperCamelCase	(	) ->List[str]:
 snake_case				=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
 snake_case				=			Image.open(requests.get(a					,   stream=a     ).raw     )
 return im

@torch.no_grad()
def    __UpperCamelCase	(	a    : Any					,   a    : Any     ) ->Dict:
 snake_case				=			ViTConfig()
 snake_case				=			False
 # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
 if vit_name[-5:] == "in21k":
  snake_case				=			True
  snake_case				=			int(vit_name[-12:-10]     )
  snake_case				=			int(vit_name[-9:-6]     )
 else:
  snake_case				=			1000
  snake_case				=			'''huggingface/label-files'''
  snake_case				=			'''imagenet-1k-id2label.json'''
  snake_case				=			json.load(open(hf_hub_download(a					,   a					,   repo_type='''dataset'''     )					,   '''r'''     )     )
  snake_case				=			{int(a     ): v for k, v in idalabel.items()}
  snake_case				=			idalabel
  snake_case				=			{v: k for k, v in idalabel.items()}
  snake_case				=			int(vit_name[-6:-4]     )
  snake_case				=			int(vit_name[-3:]     )
 # size of the architecture
 if "deit" in vit_name:
  if vit_name[9:].startswith('''tiny'''     ):
   snake_case				=			192
   snake_case				=			768
   snake_case				=			12
   snake_case				=			3
  elif vit_name[9:].startswith('''small'''     ):
   snake_case				=			384
   snake_case				=			1536
   snake_case				=			12
   snake_case				=			6
  else:
   pass
 else:
  if vit_name[4:].startswith('''small'''     ):
   snake_case				=			768
   snake_case				=			2304
   snake_case				=			8
   snake_case				=			8
  elif vit_name[4:].startswith('''base'''     ):
   pass
  elif vit_name[4:].startswith('''large'''     ):
   snake_case				=			1024
   snake_case				=			4096
   snake_case				=			24
   snake_case				=			16
  elif vit_name[4:].startswith('''huge'''     ):
   snake_case				=			1280
   snake_case				=			5120
   snake_case				=			32
   snake_case				=			16

    # load original model from timm
 snake_case				=			timm.create_model(a					,   pretrained=a     )
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 snake_case				=			timm_model.state_dict()
 if base_model:
  remove_classification_head_(a     )
 snake_case				=			create_rename_keys(a					,   a     )
 for src, dest in rename_keys:
  rename_key(a					,   a					,   a     )
 read_in_q_k_v(a					,   a					,   a     )

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  snake_case				=			ViTModel(a     ).eval()
 else:
  snake_case				=			ViTForImageClassification(a     ).eval()
 model.load_state_dict(a     )

 # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
 if "deit" in vit_name:
  snake_case				=			DeiTImageProcessor(size=config.image_size     )
 else:
  snake_case				=			ViTImageProcessor(size=config.image_size     )
 snake_case				=			image_processor(images=prepare_img()					,   return_tensors='''pt'''     )
 snake_case				=			encoding['''pixel_values''']
 snake_case				=			model(a     )

 if base_model:
  snake_case				=			timm_model.forward_features(a     )
  assert timm_pooled_output.shape == outputs.pooler_output.shape
  assert torch.allclose(a					,   outputs.pooler_output					,   atol=1e-3     )
 else:
  snake_case				=			timm_model(a     )
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(a					,   outputs.logits					,   atol=1e-3     )

 Path(a     ).mkdir(exist_ok=a     )
 print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""     )
 model.save_pretrained(a     )
 print(f"""Saving image processor to {pytorch_dump_folder_path}"""     )
 image_processor.save_pretrained(a     )


if __name__ == "__main__":
   _lowercase       						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--vit_name',
       default='vit_base_patch16_224',
       type=str,
       help='Name of the ViT 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.'
   )

   _lowercase       						=			parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)








'''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())

