



"""simple docstring"""

import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





					if "img_encoder.pos_embed" in name:
										snake_case_					:		Optional[Any] 					=					name.replace("""img_encoder.pos_embed"""				,    """vision_model.embeddings.position_embeddings"""			)
					if "img_encoder.patch_embed.proj" in name:
										snake_case_					:		List[Any] 					=					name.replace("""img_encoder.patch_embed.proj"""				,    """vision_model.embeddings.patch_embeddings.projection"""			)
					if "img_encoder.patch_embed.norm" in name:
										snake_case_					:		Union[str, Any] 					=					name.replace("""img_encoder.patch_embed.norm"""				,    """vision_model.embeddings.layernorm"""			)
					if "img_encoder.layers" in name:
										snake_case_					:		List[Any] 					=					name.replace("""img_encoder.layers"""				,    """vision_model.encoder.stages"""			)
					if "blocks" in name and "res" not in name:
										snake_case_					:		Dict 					=					name.replace("""blocks"""				,    """layers"""			)
					if "attn" in name and "pre_assign" not in name:
										snake_case_					:		Union[str, Any] 					=					name.replace("""attn"""				,    """self_attn"""			)
					if "proj" in name and "self_attn" in name and "text" not in name:
										snake_case_					:		Union[str, Any] 					=					name.replace("""proj"""				,    """out_proj"""			)
					if "pre_assign_attn.attn.proj" in name:
										snake_case_					:		Dict 					=					name.replace("""pre_assign_attn.attn.proj"""				,    """pre_assign_attn.attn.out_proj"""			)
					if "norm1" in name:
										snake_case_					:		Optional[Any] 					=					name.replace("""norm1"""				,    """layer_norm1"""			)
					if "norm2" in name and "pre_assign" not in name:
										snake_case_					:		int 					=					name.replace("""norm2"""				,    """layer_norm2"""			)
					if "img_encoder.norm" in name:
										snake_case_					:		Optional[Any] 					=					name.replace("""img_encoder.norm"""				,    """vision_model.layernorm"""			)
					# text encoder
					if "text_encoder.token_embedding" in name:
										snake_case_					:		int 					=					name.replace("""text_encoder.token_embedding"""				,    """text_model.embeddings.token_embedding"""			)
					if "text_encoder.positional_embedding" in name:
										snake_case_					:		Tuple 					=					name.replace("""text_encoder.positional_embedding"""				,    """text_model.embeddings.position_embedding.weight"""			)
					if "text_encoder.transformer.resblocks." in name:
										snake_case_					:		str 					=					name.replace("""text_encoder.transformer.resblocks."""				,    """text_model.encoder.layers."""			)
					if "ln_1" in name:
										snake_case_					:		Optional[Any] 					=					name.replace("""ln_1"""				,    """layer_norm1"""			)
					if "ln_2" in name:
										snake_case_					:		int 					=					name.replace("""ln_2"""				,    """layer_norm2"""			)
					if "c_fc" in name:
										snake_case_					:		Union[str, Any] 					=					name.replace("""c_fc"""				,    """fc1"""			)
					if "c_proj" in name:
										snake_case_					:		Union[str, Any] 					=					name.replace("""c_proj"""				,    """fc2"""			)
					if "text_encoder" in name:
										snake_case_					:		Optional[int] 					=					name.replace("""text_encoder"""				,    """text_model"""			)
					if "ln_final" in name:
										snake_case_					:		Any 					=					name.replace("""ln_final"""				,    """final_layer_norm"""			)
					# projection layers
					if "img_projector.linear_hidden." in name:
										snake_case_					:		List[str] 					=					name.replace("""img_projector.linear_hidden."""				,    """visual_projection."""			)
					if "img_projector.linear_out." in name:
										snake_case_					:		int 					=					name.replace("""img_projector.linear_out."""				,    """visual_projection.3."""			)
					if "text_projector.linear_hidden" in name:
										snake_case_					:		Dict 					=					name.replace("""text_projector.linear_hidden"""				,    """text_projection"""			)
					if "text_projector.linear_out" in name:
										snake_case_					:		Dict 					=					name.replace("""text_projector.linear_out"""				,    """text_projection.3"""			)

					return name







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[int]				,    SCREAMING_SNAKE_CASE__ :  Optional[int]			):



					"""simple docstring"""





					for key in orig_state_dict.copy().keys():
										snake_case_					:		Dict 					=					orig_state_dict.pop(SCREAMING_SNAKE_CASE__			)

										if "qkv" in key:
															# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
															# we need to split them up into separate matrices/vectors
															snake_case_					:		Dict 					=					key.split("""."""			)
															snake_case_					,			snake_case_					:		Tuple 					=					int(key_split[2]			), int(key_split[4]			)
															snake_case_					:		List[Any] 					=					config.vision_config.hidden_size
															if "weight" in key:
																				snake_case_					:		List[str] 					=					val[:dim, :]
																				snake_case_					:		Union[str, Any] 					=					val[dim : dim * 2, :]
																				snake_case_					:		Tuple 					=					val[-dim:, :]
															else:
																				snake_case_					:		Optional[int] 					=					val[:dim]
																				snake_case_					:		Optional[Any] 					=					val[dim : dim * 2]
																				snake_case_					:		Union[str, Any] 					=					val[-dim:]
										elif "in_proj" in key:
															# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
															# we need to split them up into separate matrices/vectors
															snake_case_					:		Any 					=					key.split("""."""			)
															snake_case_					:		Optional[Any] 					=					int(key_split[3]			)
															snake_case_					:		Optional[int] 					=					config.text_config.hidden_size
															if "weight" in key:
																				snake_case_					:		Tuple 					=					val[:dim, :]
																				snake_case_					:		Optional[Any] 					=					val[
																				    dim : dim * 2, :
																				]
																				snake_case_					:		Dict 					=					val[-dim:, :]
															else:
																				snake_case_					:		Optional[int] 					=					val[:dim]
																				snake_case_					:		Any 					=					val[dim : dim * 2]
																				snake_case_					:		str 					=					val[-dim:]
										else:
															snake_case_					:		str 					=					rename_key(SCREAMING_SNAKE_CASE__			)
															# squeeze if necessary
															if (
															    "text_projection.0" in new_name
															    or "text_projection.3" in new_name
															    or "visual_projection.0" in new_name
															    or "visual_projection.3" in new_name
															):
																				snake_case_					:		str 					=					val.squeeze_()
															else:
																				snake_case_					:		Dict 					=					val

					return orig_state_dict







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		Dict 					=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
					snake_case_					:		Tuple 					=					Image.open(requests.get(SCREAMING_SNAKE_CASE__				,    stream=SCREAMING_SNAKE_CASE__			).raw			)
					return im







@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Optional[int]="groupvit-gcc-yfcc"				,    SCREAMING_SNAKE_CASE__ :  Union[str, Any]=False			):



					"""simple docstring"""





					snake_case_					:		Any 					=					GroupViTConfig()
					snake_case_					:		str 					=					GroupViTModel(SCREAMING_SNAKE_CASE__			).eval()

					snake_case_					:		Union[str, Any] 					=					torch.load(SCREAMING_SNAKE_CASE__				,    map_location="""cpu"""			)["""model"""]
					snake_case_					:		Union[str, Any] 					=					convert_state_dict(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					snake_case_					,			snake_case_					:		int 					=					model.load_state_dict(SCREAMING_SNAKE_CASE__				,    strict=SCREAMING_SNAKE_CASE__			)
					assert missing_keys == ["text_model.embeddings.position_ids"]
					assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(SCREAMING_SNAKE_CASE__			) == 0)

					# verify result
					snake_case_					:		str 					=					CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32"""			)
					snake_case_					:		int 					=					prepare_img()
					snake_case_					:		Optional[int] 					=					processor(text=["""a photo of a cat""", """a photo of a dog"""]				,    images=SCREAMING_SNAKE_CASE__				,    padding=SCREAMING_SNAKE_CASE__				,    return_tensors="""pt"""			)

					with torch.no_grad():
										snake_case_					:		Union[str, Any] 					=					model(**SCREAMING_SNAKE_CASE__			)

					if model_name == "groupvit-gcc-yfcc":
										snake_case_					:		Union[str, Any] 					=					torch.tensor([[13.3523, 6.3629]]			)
					elif model_name == "groupvit-gcc-redcaps":
										snake_case_					:		List[Any] 					=					torch.tensor([[16.1873, 8.6230]]			)
					else:
										raise ValueError(f'Model name {model_name} not supported.'			)
					assert torch.allclose(outputs.logits_per_image				,    SCREAMING_SNAKE_CASE__				,    atol=1E-3			)

					processor.save_pretrained(SCREAMING_SNAKE_CASE__			)
					model.save_pretrained(SCREAMING_SNAKE_CASE__			)
					print("""Successfully saved processor and model to"""				,    SCREAMING_SNAKE_CASE__			)

					if push_to_hub:
										print("""Pushing to the hub..."""			)
										processor.push_to_hub(SCREAMING_SNAKE_CASE__				,    organization="""nielsr"""			)
										model.push_to_hub(SCREAMING_SNAKE_CASE__				,    organization="""nielsr"""			)


if __name__ == "__main__":
			a_			   =					argparse.ArgumentParser()
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to dump the processor and PyTorch model.'''
			)
			parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to GroupViT checkpoint''')
			parser.add_argument(
			    '''--model_name''',
			    default='''groupvit-gccy-fcc''',
			    type=str,
			    help='''Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'''',
			)
			parser.add_argument(
			    '''--push_to_hub''',
			    action='''store_true''',
			    help='''Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.''',
			)
			a_			   =					parser.parse_args()

			convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)









"""simple docstring"""

import argparse
import copy







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Any			):



					"""simple docstring"""





					snake_case_					:		List[Any] 					=					{}

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

					return dict_of_neighbours







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  str			):



					"""simple docstring"""





					with open(SCREAMING_SNAKE_CASE__			) as f:
										snake_case_					:		Optional[Any] 					=					f.read(1			)
					snake_case_					:		Union[str, Any] 					=					start_node

					snake_case_					:		Dict 					=					[]

					snake_case_					:		Union[str, Any] 					=					start_node

					snake_case_					:		Tuple 					=					0
					while visiting not in first_solution:
										snake_case_					:		int 					=					1_0_0_0_0
										for k in dict_of_neighbours[visiting]:
															if int(k[1]			) < int(SCREAMING_SNAKE_CASE__			) and k[0] not in first_solution:
																				snake_case_					:		Union[str, Any] 					=					k[1]
																				snake_case_					:		Any 					=					k[0]

										first_solution.append(SCREAMING_SNAKE_CASE__			)
										snake_case_					:		Tuple 					=					distance_of_first_solution + int(SCREAMING_SNAKE_CASE__			)
										snake_case_					:		List[str] 					=					best_node

					first_solution.append(SCREAMING_SNAKE_CASE__			)

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

					snake_case_					:		int 					=					(
					    distance_of_first_solution
					    + int(dict_of_neighbours[first_solution[-2]][position][1]			)
					    - 1_0_0_0_0
					)
					return first_solution, distance_of_first_solution







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Union[str, Any]				,    SCREAMING_SNAKE_CASE__ :  Optional[int]			):



					"""simple docstring"""





					snake_case_					:		Union[str, Any] 					=					[]

					for n in solution[1:-1]:
										snake_case_					:		str 					=					solution.index(SCREAMING_SNAKE_CASE__			)
										for kn in solution[1:-1]:
															snake_case_					:		Tuple 					=					solution.index(SCREAMING_SNAKE_CASE__			)
															if n == kn:
																				continue

															snake_case_					:		Optional[Any] 					=					copy.deepcopy(SCREAMING_SNAKE_CASE__			)
															snake_case_					:		int 					=					kn
															snake_case_					:		Dict 					=					n

															snake_case_					:		Optional[int] 					=					0

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

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

					snake_case_					:		Optional[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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  List[Any]			):



					"""simple docstring"""





					snake_case_					:		Dict 					=					1
					snake_case_					:		List[Any] 					=					first_solution
					snake_case_					:		List[Any] 					=					[]
					snake_case_					:		Optional[Any] 					=					distance_of_first_solution
					snake_case_					:		Dict 					=					solution

					while count <= iters:
										snake_case_					:		List[str] 					=					find_neighborhood(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
										snake_case_					:		List[Any] 					=					0
										snake_case_					:		List[Any] 					=					neighborhood[index_of_best_solution]
										snake_case_					:		Union[str, Any] 					=					len(SCREAMING_SNAKE_CASE__			) - 1

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

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

										snake_case_					:		List[str] 					=					count + 1

					return best_solution_ever, best_cost







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[Any]=None			):



					"""simple docstring"""





					snake_case_					:		Tuple 					=					generate_neighbours(args.File			)

					snake_case_					,			snake_case_					:		Optional[Any] 					=					generate_first_solution(
					    args.File				,    SCREAMING_SNAKE_CASE__			)

					snake_case_					,			snake_case_					:		Dict 					=					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__":
			a_			   =					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())




