

'''simple docstring'''
import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)


def        _lowerCAmelCase					(						_UpperCamelCase						:     List[str]							, _UpperCamelCase						:     Any				)      ->			str:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    ='https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
     _SCREAMING_SNAKE_CASE							    =Image.open(requests.get(_UpperCamelCase							, stream=_UpperCamelCase				).raw				).convert('RGB'				)

     _SCREAMING_SNAKE_CASE							    =transforms.Compose(
         [
             transforms.Resize((image_size, image_size)							, interpolation=InterpolationMode.BICUBIC				),
             transforms.ToTensor(),
             transforms.Normalize((0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73)							, (0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11)				),
         ]				)
     _SCREAMING_SNAKE_CASE							    =transform(_UpperCamelCase				).unsqueeze(0				).to(_UpperCamelCase				)
     return image


def        _lowerCAmelCase					(						_UpperCamelCase						:     Dict				)      ->			str:
     """simple docstring"""




     if "visual_encoder" in key:
          _SCREAMING_SNAKE_CASE							    =re.sub('visual_encoder*'							, 'vision_model.encoder'							, _UpperCamelCase				)
     if "blocks" in key:
          _SCREAMING_SNAKE_CASE							    =re.sub(r'blocks'							, 'layers'							, _UpperCamelCase				)
     if "attn" in key:
          _SCREAMING_SNAKE_CASE							    =re.sub(r'attn'							, 'self_attn'							, _UpperCamelCase				)
     if "norm1" in key:
          _SCREAMING_SNAKE_CASE							    =re.sub(r'norm1'							, 'layer_norm1'							, _UpperCamelCase				)
     if "norm2" in key:
          _SCREAMING_SNAKE_CASE							    =re.sub(r'norm2'							, 'layer_norm2'							, _UpperCamelCase				)
     if "encoder.norm" in key:
          _SCREAMING_SNAKE_CASE							    =re.sub(r'encoder.norm'							, 'post_layernorm'							, _UpperCamelCase				)
     if "encoder.patch_embed.proj" in key:
          _SCREAMING_SNAKE_CASE							    =re.sub(r'encoder.patch_embed.proj'							, 'embeddings.patch_embedding'							, _UpperCamelCase				)

     if "encoder.pos_embed" in key:
          _SCREAMING_SNAKE_CASE							    =re.sub(r'encoder.pos_embed'							, 'embeddings.position_embedding'							, _UpperCamelCase				)
     if "encoder.cls_token" in key:
          _SCREAMING_SNAKE_CASE							    =re.sub(r'encoder.cls_token'							, 'embeddings.class_embedding'							, _UpperCamelCase				)

     if "self_attn" in key:
          _SCREAMING_SNAKE_CASE							    =re.sub(r'self_attn.proj'							, 'self_attn.projection'							, _UpperCamelCase				)

     return key


@torch.no_grad()
def        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     int=None				)      ->			Tuple:
     """simple docstring"""




     if config_path is not None:
          _SCREAMING_SNAKE_CASE							    =BlipConfig.from_pretrained(_UpperCamelCase				)
     else:
          _SCREAMING_SNAKE_CASE							    =BlipConfig(projection_dim=5_12							, text_config={}							, vision_config={}				)

     _SCREAMING_SNAKE_CASE							    =BlipForConditionalGeneration(_UpperCamelCase				).eval()

     _SCREAMING_SNAKE_CASE							    ='https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'

     _SCREAMING_SNAKE_CASE							    =blip_decoder(pretrained=_UpperCamelCase							, image_size=3_84							, vit='base'				)
     _SCREAMING_SNAKE_CASE							    =pt_model.eval()

     _SCREAMING_SNAKE_CASE							    =pt_model.state_dict()
     for key in modified_state_dict.copy():
          _SCREAMING_SNAKE_CASE							    =modified_state_dict.pop(_UpperCamelCase				)
          _SCREAMING_SNAKE_CASE							    =rename_key(_UpperCamelCase				)
          _SCREAMING_SNAKE_CASE							    =value

     hf_model.load_state_dict(_UpperCamelCase				)

     _SCREAMING_SNAKE_CASE							    =3_84
     _SCREAMING_SNAKE_CASE							    =load_demo_image(image_size=_UpperCamelCase							, device='cpu'				)
     _SCREAMING_SNAKE_CASE							    =BertTokenizer.from_pretrained('bert-base-uncased'				)
     _SCREAMING_SNAKE_CASE							    =tokenizer(['a picture of']				).input_ids

     _SCREAMING_SNAKE_CASE							    =hf_model.generate(_UpperCamelCase							, _UpperCamelCase				)

     assert out[0].tolist() == [3_05_22, 10_37, 38_61, 19_97, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

     _SCREAMING_SNAKE_CASE							    =hf_model.generate(_UpperCamelCase				)

     assert out[0].tolist() == [3_05_22, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

     if pytorch_dump_folder_path is not None:
          hf_model.save_pretrained(_UpperCamelCase				)

     # model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
     _SCREAMING_SNAKE_CASE							    =(
         'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
     )

     _SCREAMING_SNAKE_CASE							    =blip_vqa(pretrained=_UpperCamelCase							, image_size=_UpperCamelCase							, vit='base'				)
     vqa_model.eval()

     _SCREAMING_SNAKE_CASE							    =vqa_model.state_dict()
     for key in modified_state_dict.copy():
          _SCREAMING_SNAKE_CASE							    =modified_state_dict.pop(_UpperCamelCase				)
          _SCREAMING_SNAKE_CASE							    =rename_key(_UpperCamelCase				)
          _SCREAMING_SNAKE_CASE							    =value

     _SCREAMING_SNAKE_CASE							    =BlipForQuestionAnswering(_UpperCamelCase				)

     hf_vqa_model.load_state_dict(_UpperCamelCase				)

     _SCREAMING_SNAKE_CASE							    =['How many dogs are in this image?']
     _SCREAMING_SNAKE_CASE							    =tokenizer(_UpperCamelCase							, return_tensors='pt'				).input_ids

     _SCREAMING_SNAKE_CASE							    =hf_vqa_model.generate(_UpperCamelCase							, _UpperCamelCase				)
     print(tokenizer.decode(answer[0]				)				)

     assert tokenizer.decode(answer[0]				) == "[UNK] 1 [SEP]"
     if pytorch_dump_folder_path is not None:
          hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '_vqa'				)

     _SCREAMING_SNAKE_CASE							    ='https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'

     _SCREAMING_SNAKE_CASE							    =blip_itm(pretrained=_UpperCamelCase							, image_size=_UpperCamelCase							, vit='base'				)
     itm_model.eval()

     _SCREAMING_SNAKE_CASE							    =itm_model.state_dict()
     for key in modified_state_dict.copy():
          _SCREAMING_SNAKE_CASE							    =modified_state_dict.pop(_UpperCamelCase				)
          _SCREAMING_SNAKE_CASE							    =rename_key(_UpperCamelCase				)
          _SCREAMING_SNAKE_CASE							    =value

     _SCREAMING_SNAKE_CASE							    =BlipForImageTextRetrieval(_UpperCamelCase				)

     _SCREAMING_SNAKE_CASE							    =['A picture of a woman with a dog sitting in a beach']
     _SCREAMING_SNAKE_CASE							    =tokenizer(
         _UpperCamelCase							, return_tensors='pt'							, padding='max_length'							, truncation=_UpperCamelCase							, max_length=35							, ).input_ids

     hf_itm_model.load_state_dict(_UpperCamelCase				)
     hf_itm_model.eval()

     _SCREAMING_SNAKE_CASE							    =hf_itm_model(_UpperCamelCase							, _UpperCamelCase							, use_itm_head=_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =hf_itm_model(_UpperCamelCase							, _UpperCamelCase							, use_itm_head=_UpperCamelCase				)

     assert out[0].item() == 0.21_10_68_74_94_27_79_54
     assert torch.nn.functional.softmax(out_itm[0]							, dim=1				)[:, 1].item() == 0.4_56_98_84_53_86_50_51_27

     if pytorch_dump_folder_path is not None:
          hf_itm_model.save_pretrained(pytorch_dump_folder_path + '_itm'				)


if __name__ == "__main__":
     lowerCamelCase :      Dict					 =     argparse.ArgumentParser()
     parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
     parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
     lowerCamelCase :      int					 =     parser.parse_args()

     convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)


'''simple docstring'''







import argparse
import copy
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Optional[int]							):



						"""simple docstring"""





						lowercase_	:     List[Any]													=       {}

						with open(__SCREAMING_SNAKE_CASE							) as f:
												for line in f:
																		if line.split()[0] not in dict_of_neighbours:
																								lowercase_	:     Union[str, Any]													=       []
																								_list.append([line.split()[1], line.split()[2]]							)
																								lowercase_	:     str													=       _list
																		else:
																								dict_of_neighbours[line.split()[0]].append(
																								    [line.split()[1], line.split()[2]]							)
																		if line.split()[1] not in dict_of_neighbours:
																								lowercase_	:     Optional[int]													=       []
																								_list.append([line.split()[0], line.split()[2]]							)
																								lowercase_	:     Dict													=       _list
																		else:
																								dict_of_neighbours[line.split()[1]].append(
																								    [line.split()[0], line.split()[2]]							)

						return dict_of_neighbours
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: Tuple							):



						"""simple docstring"""





						with open(__SCREAMING_SNAKE_CASE							) as f:
												lowercase_	:     List[str]													=       f.read(1							)
						lowercase_	:     Optional[int]													=       start_node

						lowercase_	:     Any													=       []

						lowercase_	:     List[str]													=       start_node

						lowercase_	:     Optional[Any]													=       0
						while visiting not in first_solution:
												lowercase_	:     Any													=       10000
												for k in dict_of_neighbours[visiting]:
																		if int(k[1]							) < int(__SCREAMING_SNAKE_CASE							) and k[0] not in first_solution:
																								lowercase_	:     List[Any]													=       k[1]
																								lowercase_	:     List[Any]													=       k[0]

												first_solution.append(__SCREAMING_SNAKE_CASE							)
												lowercase_	:     Optional[Any]													=       distance_of_first_solution + int(__SCREAMING_SNAKE_CASE							)
												lowercase_	:     int													=       best_node

						first_solution.append(__SCREAMING_SNAKE_CASE							)

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

						lowercase_	:     Optional[Any]													=       (
						    distance_of_first_solution
						    + int(dict_of_neighbours[first_solution[-2]][position][1]							)
						    - 10000
						)
						return first_solution, distance_of_first_solution
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Optional[Any]      ,      __SCREAMING_SNAKE_CASE					: Union[str, Any]							):



						"""simple docstring"""





						lowercase_	:     Tuple													=       []

						for n in solution[1:-1]:
												lowercase_	:     List[str]													=       solution.index(__SCREAMING_SNAKE_CASE							)
												for kn in solution[1:-1]:
																		lowercase_	:     Any													=       solution.index(__SCREAMING_SNAKE_CASE							)
																		if n == kn:
																								continue

																		lowercase_	:     Dict													=       copy.deepcopy(__SCREAMING_SNAKE_CASE							)
																		lowercase_	:     Dict													=       kn
																		lowercase_	:     List[Any]													=       n

																		lowercase_	:     str													=       0

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

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

						lowercase_	:     Union[str, Any]													=       len(neighborhood_of_solution[0]							) - 1

						neighborhood_of_solution.sort(key=lambda __SCREAMING_SNAKE_CASE							: x[index_of_last_item_in_the_list]							)
						return neighborhood_of_solution
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Tuple      ,      __SCREAMING_SNAKE_CASE					: List[Any]      ,      __SCREAMING_SNAKE_CASE					: Optional[int]      ,      __SCREAMING_SNAKE_CASE					: Optional[int]      ,      __SCREAMING_SNAKE_CASE					: Optional[Any]							):



						"""simple docstring"""





						lowercase_	:     Optional[int]													=       1
						lowercase_	:     List[str]													=       first_solution
						lowercase_	:     Dict													=       []
						lowercase_	:     List[str]													=       distance_of_first_solution
						lowercase_	:     Optional[Any]													=       solution

						while count <= iters:
												lowercase_	:     int													=       find_neighborhood(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)
												lowercase_	:     Any													=       0
												lowercase_	:     Dict													=       neighborhood[index_of_best_solution]
												lowercase_	:     Optional[Any]													=       len(__SCREAMING_SNAKE_CASE							) - 1

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

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

												lowercase_	:     List[Any]													=       count + 1

						return best_solution_ever, best_cost
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[str]=None							):



						"""simple docstring"""





						lowercase_	:     Any													=       generate_neighbours(args.File							)

						lowercase_						, lowercase_	:     Union[str, Any]													=       generate_first_solution(
						    args.File      ,      __SCREAMING_SNAKE_CASE							)

						lowercase_						, lowercase_	:     Optional[int]													=       tabu_search(
						    __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      args.Iterations      ,      args.Size      ,      )

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


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



