





import argparse
import copy


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	int:
    lowerCamelCase__       : Union[str, Any]   					=						{}

    with open(_UpperCAmelCase					) as f:
        for line in f:
            if line.split()[0] not in dict_of_neighbours:
                lowerCamelCase__       : Dict   					=						[]
                _list.append([line.split()[1], line.split()[2]]					)
                lowerCamelCase__       : int   					=						_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__       : List[Any]   					=						[]
                _list.append([line.split()[0], line.split()[2]]					)
                lowerCamelCase__       : Tuple   					=						_list
            else:
                dict_of_neighbours[line.split()[1]].append(
                    [line.split()[0], line.split()[2]]					)

    return dict_of_neighbours


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	int:

    with open(_UpperCAmelCase					) as f:
        lowerCamelCase__       : Dict   					=						f.read(1					)
    lowerCamelCase__       : Optional[Any]   					=						start_node

    lowerCamelCase__       : List[Any]   					=						[]

    lowerCamelCase__       : List[Any]   					=						start_node

    lowerCamelCase__       : Dict   					=						0
    while visiting not in first_solution:
        lowerCamelCase__       : Union[str, Any]   					=						1_0000
        for k in dict_of_neighbours[visiting]:
            if int(k[1]					) < int(_UpperCAmelCase					) and k[0] not in first_solution:
                lowerCamelCase__       : Tuple   					=						k[1]
                lowerCamelCase__       : Optional[Any]   					=						k[0]

        first_solution.append(_UpperCAmelCase					)
        lowerCamelCase__       : Union[str, Any]   					=						distance_of_first_solution + int(_UpperCAmelCase					)
        lowerCamelCase__       : int   					=						best_node

    first_solution.append(_UpperCAmelCase					)

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

    lowerCamelCase__       : Union[str, Any]   					=						(
        distance_of_first_solution
        + int(dict_of_neighbours[first_solution[-2]][position][1]					)
        - 1_0000
    )
    return first_solution, distance_of_first_solution


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	Optional[Any]:
    lowerCamelCase__       : int   					=						[]

    for n in solution[1:-1]:
        lowerCamelCase__       : List[Any]   					=						solution.index(_UpperCAmelCase					)
        for kn in solution[1:-1]:
            lowerCamelCase__       : Union[str, Any]   					=						solution.index(_UpperCAmelCase					)
            if n == kn:
                continue

            lowerCamelCase__       : Optional[int]   					=						copy.deepcopy(_UpperCAmelCase					)
            lowerCamelCase__       : Optional[Any]   					=						kn
            lowerCamelCase__       : Optional[Any]   					=						n

            lowerCamelCase__       : Any   					=						0

            for k in _tmp[:-1]:
                lowerCamelCase__       : Union[str, Any]   					=						_tmp[_tmp.index(_UpperCAmelCase					) + 1]
                for i in dict_of_neighbours[k]:
                    if i[0] == next_node:
                        lowerCamelCase__       : List[str]   					=						distance + int(i[1]					)
            _tmp.append(_UpperCAmelCase					)

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

    lowerCamelCase__       : str   					=						len(neighborhood_of_solution[0]					) - 1

    neighborhood_of_solution.sort(key=lambda _UpperCAmelCase					: x[index_of_last_item_in_the_list]					)
    return neighborhood_of_solution


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	Any:
    lowerCamelCase__       : Union[str, Any]   					=						1
    lowerCamelCase__       : Optional[int]   					=						first_solution
    lowerCamelCase__       : Dict   					=						[]
    lowerCamelCase__       : Tuple   					=						distance_of_first_solution
    lowerCamelCase__       : Dict   					=						solution

    while count <= iters:
        lowerCamelCase__       : List[Any]   					=						find_neighborhood(_UpperCAmelCase		,      _UpperCAmelCase					)
        lowerCamelCase__       : Optional[Any]   					=						0
        lowerCamelCase__       : Tuple   					=						neighborhood[index_of_best_solution]
        lowerCamelCase__       : List[str]   					=						len(_UpperCAmelCase					) - 1

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

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

        lowerCamelCase__       : List[Any]   					=						count + 1

    return best_solution_ever, best_cost


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase=None					)     ->	Dict:
    lowerCamelCase__       : Dict   					=						generate_neighbours(args.File					)

    lowerCamelCase__					,				lowerCamelCase__       : str   					=						generate_first_solution(
        args.File		,      _UpperCAmelCase					)

    lowerCamelCase__					,				lowerCamelCase__       : Optional[Any]   					=						tabu_search(
        _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      args.Iterations		,      args.Size		,      )

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


if __name__ == "__main__":
    _UpperCAmelCase							:			List[str]        =							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())












import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	Dict:
    lowerCamelCase__       : Optional[int]   					=						[]
    embed.append(
        (
            F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
            F"""stage{idx}.patch_embed.proj.weight""",
        )					)
    embed.append(
        (
            F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
            F"""stage{idx}.patch_embed.proj.bias""",
        )					)
    embed.append(
        (
            F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
            F"""stage{idx}.patch_embed.norm.weight""",
        )					)
    embed.append(
        (
            F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
            F"""stage{idx}.patch_embed.norm.bias""",
        )					)
    return embed


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	List[str]:
    lowerCamelCase__       : Optional[int]   					=						[]
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
            F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
            F"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
            F"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
            F"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
            F"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
            F"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
            F"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
            F"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
        )					)
    attention_weights.append(
        (
            F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
            F"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
        )					)
    attention_weights.append(
        (F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""")					)
    attention_weights.append(
        (F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""")					)
    attention_weights.append(
        (F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""")					)
    attention_weights.append(
        (F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""")					)
    attention_weights.append(
        (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", F"""stage{idx}.blocks.{cnt}.norm1.weight""")					)
    attention_weights.append(
        (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", F"""stage{idx}.blocks.{cnt}.norm1.bias""")					)
    attention_weights.append(
        (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", F"""stage{idx}.blocks.{cnt}.norm2.weight""")					)
    attention_weights.append(
        (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", F"""stage{idx}.blocks.{cnt}.norm2.bias""")					)
    return attention_weights


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	str:
    lowerCamelCase__       : Union[str, Any]   					=						[]
    token.append((F"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token')					)
    return token


def 			SCREAMING_SNAKE_CASE (			)     ->	Dict:
    lowerCamelCase__       : Tuple   					=						[]
    head.append(('layernorm.weight', 'norm.weight')					)
    head.append(('layernorm.bias', 'norm.bias')					)
    head.append(('classifier.weight', 'head.weight')					)
    head.append(('classifier.bias', 'head.bias')					)
    return head


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	Optional[int]:
    lowerCamelCase__       : Union[str, Any]   					=						'imagenet-1k-id2label.json'
    lowerCamelCase__       : Tuple   					=						1000

    lowerCamelCase__       : Tuple   					=						'huggingface/label-files'
    lowerCamelCase__       : Optional[int]   					=						num_labels
    lowerCamelCase__       : Optional[Any]   					=						json.load(open(cached_download(hf_hub_url(_UpperCAmelCase		,      _UpperCAmelCase		,      repo_type='dataset'					)					)		,      'r'					)					)
    lowerCamelCase__       : Optional[int]   					=						{int(_UpperCAmelCase					): v for k, v in idalabel.items()}

    lowerCamelCase__       : List[Any]   					=						idalabel
    lowerCamelCase__       : Optional[Any]   					=						{v: k for k, v in idalabel.items()}

    lowerCamelCase__       : int   					=						CvtConfig(num_labels=_UpperCAmelCase		,      idalabel=_UpperCAmelCase		,      labelaid=_UpperCAmelCase					)

    # For depth size 13 (13 = 1+2+10)
    if cvt_model.rsplit('/'		,      1					)[-1][4:6] == "13":
        lowerCamelCase__       : Dict   					=						[1, 2, 10]

    # For depth size 21 (21 = 1+4+16)
    elif cvt_model.rsplit('/'		,      1					)[-1][4:6] == "21":
        lowerCamelCase__       : List[str]   					=						[1, 4, 16]

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        lowerCamelCase__       : Tuple   					=						[2, 2, 20]
        lowerCamelCase__       : Optional[int]   					=						[3, 12, 16]
        lowerCamelCase__       : int   					=						[192, 768, 1024]

    lowerCamelCase__       : Tuple   					=						CvtForImageClassification(_UpperCAmelCase					)
    lowerCamelCase__       : str   					=						AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'					)
    lowerCamelCase__       : Optional[Any]   					=						image_size
    lowerCamelCase__       : int   					=						torch.load(_UpperCAmelCase		,      map_location=torch.device('cpu'					)					)

    lowerCamelCase__       : Dict   					=						OrderedDict()
    lowerCamelCase__       : Union[str, Any]   					=						[]

    for idx in range(len(config.depth					)					):
        if config.cls_token[idx]:
            lowerCamelCase__       : str   					=						list_of_state_dict + cls_token(_UpperCAmelCase					)
        lowerCamelCase__       : Tuple   					=						list_of_state_dict + embeddings(_UpperCAmelCase					)
        for cnt in range(config.depth[idx]					):
            lowerCamelCase__       : List[str]   					=						list_of_state_dict + attention(_UpperCAmelCase		,      _UpperCAmelCase					)

    lowerCamelCase__       : Optional[Any]   					=						list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(_UpperCAmelCase					)
    for i in range(len(_UpperCAmelCase					)					):
        lowerCamelCase__       : List[str]   					=						original_weights[list_of_state_dict[i][1]]

    model.load_state_dict(_UpperCAmelCase					)
    model.save_pretrained(_UpperCAmelCase					)
    image_processor.save_pretrained(_UpperCAmelCase					)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
    _UpperCAmelCase							:			int        =							argparse.ArgumentParser()
    parser.add_argument(
        """--cvt_model""",
        default="""cvt-w24""",
        type=str,
        help="""Name of the cvt model you'd like to convert.""",
    )
    parser.add_argument(
        """--image_size""",
        default=3_84,
        type=int,
        help="""Input Image Size""",
    )
    parser.add_argument(
        """--cvt_file_name""",
        default=R"""cvtmodels\CvT-w24-384x384-IN-22k.pth""",
        type=str,
        help="""Input Image Size""",
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
    )

    _UpperCAmelCase							:			Optional[int]        =							parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)





