



"""simple docstring"""

import argparse
import copy




def __lowercase    (							_a					):
    snake_case_				:  Tuple       =			{}

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

    return dict_of_neighbours




def __lowercase    (							_a	,    _a					):

    with open(_a					) as f:
        snake_case_				:  int       =			f.read(1					)
    snake_case_				:  Optional[int]       =			start_node

    snake_case_				:  Union[str, Any]       =			[]

    snake_case_				:  Tuple       =			start_node

    snake_case_				:  Dict       =			0
    while visiting not in first_solution:
        snake_case_				:  Any       =			10_000
        for k in dict_of_neighbours[visiting]:
            if int(k[1]					) < int(_a					) and k[0] not in first_solution:
                snake_case_				:  Tuple       =			k[1]
                snake_case_				:  int       =			k[0]

        first_solution.append(_a					)
        snake_case_				:  List[Any]       =			distance_of_first_solution + int(_a					)
        snake_case_				:  List[Any]       =			best_node

    first_solution.append(_a					)

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

    snake_case_				:  List[str]       =			(
        distance_of_first_solution
        + int(dict_of_neighbours[first_solution[-2]][position][1]					)
        - 10_000
    )
    return first_solution, distance_of_first_solution




def __lowercase    (							_a	,    _a					):
    snake_case_				:  Dict       =			[]

    for n in solution[1:-1]:
        snake_case_				:  Optional[Any]       =			solution.index(_a					)
        for kn in solution[1:-1]:
            snake_case_				:  Tuple       =			solution.index(_a					)
            if n == kn:
                continue

            snake_case_				:  Optional[Any]       =			copy.deepcopy(_a					)
            snake_case_				:  Optional[int]       =			kn
            snake_case_				:  List[str]       =			n

            snake_case_				:  Any       =			0

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

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

    snake_case_				:  Optional[int]       =			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 __lowercase    (							_a	,    _a	,    _a	,    _a	,    _a					):
    snake_case_				:  List[str]       =			1
    snake_case_				:  List[str]       =			first_solution
    snake_case_				:  Any       =			[]
    snake_case_				:  List[str]       =			distance_of_first_solution
    snake_case_				:  Union[str, Any]       =			solution

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

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

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

        snake_case_				:  int       =			count + 1

    return best_solution_ever, best_cost




def __lowercase    (							_a=None					):
    snake_case_				:  Optional[Any]       =			generate_neighbours(args.File					)

    snake_case_,			snake_case_				:  List[str]       =			generate_first_solution(
        args.File	,    _a					)

    snake_case_,			snake_case_				:  Dict       =			tabu_search(
        _a	,    _a	,    _a	,    args.Iterations	,    args.Size	,    )

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


if __name__ == "__main__":
     lowercase__					:						Optional[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())










"""simple docstring"""

import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
lowercase__					:						Dict												=			logging.get_logger(__name__)

lowercase__					:						List[str]												=			{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''label_embs_concat''': '''label_embeddings_concat''',
    '''mask_emb''': '''masked_spec_embed''',
    '''spk_proj''': '''speaker_proj''',
}
lowercase__					:						Dict												=			[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]




def __lowercase    (							_a	,    _a	,    _a	,    _a	,    _a					):
    for attribute in key.split('''.'''					):
        snake_case_				:  Optional[Any]       =			getattr(_a	,    _a					)

    if weight_type is not None:
        snake_case_				:  Optional[int]       =			getattr(_a	,    _a					).shape
    else:
        snake_case_				:  Optional[int]       =			hf_pointer.shape

    if hf_shape != value.shape:
        raise ValueError(
            f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
            f" {value.shape} for {full_name}"					)

    if weight_type == "weight":
        snake_case_				:  Dict       =			value
    elif weight_type == "weight_g":
        snake_case_				:  Tuple       =			value
    elif weight_type == "weight_v":
        snake_case_				:  Tuple       =			value
    elif weight_type == "bias":
        snake_case_				:  int       =			value
    else:
        snake_case_				:  Optional[Any]       =			value

    logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."					)




def __lowercase    (							_a	,    _a					):
    snake_case_				:  Optional[int]       =			[]
    snake_case_				:  Optional[Any]       =			fairseq_model.state_dict()

    snake_case_				:  Optional[int]       =			hf_model.unispeech_sat.feature_extractor

    for name, value in fairseq_dict.items():
        snake_case_				:  int       =			False
        if "conv_layers" in name:
            load_conv_layer(
                _a	,    _a	,    _a	,    _a	,    hf_model.config.feat_extract_norm == '''group'''	,    )
            snake_case_				:  Any       =			True
        else:
            for key, mapped_key in MAPPING.items():
                snake_case_				:  Tuple       =			'''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                if key in name or key.split('''w2v_model.'''					)[-1] == name.split('''.'''					)[0]:
                    if "layer_norm_for_extract" in name and (".".join(name.split('''.'''					)[:-1]					) != key):
                        # special case since naming is very similar
                        continue
                    snake_case_				:  List[str]       =			True
                    if "*" in mapped_key:
                        snake_case_				:  List[Any]       =			name.split(_a					)[0].split('''.'''					)[-2]
                        snake_case_				:  Dict       =			mapped_key.replace('''*'''	,    _a					)
                    if "weight_g" in name:
                        snake_case_				:  Any       =			'''weight_g'''
                    elif "weight_v" in name:
                        snake_case_				:  List[str]       =			'''weight_v'''
                    elif "bias" in name:
                        snake_case_				:  List[str]       =			'''bias'''
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        snake_case_				:  Optional[int]       =			'''weight'''
                    else:
                        snake_case_				:  List[Any]       =			None
                    set_recursively(_a	,    _a	,    _a	,    _a	,    _a					)
                continue
        if not is_used:
            unused_weights.append(_a					)

    logger.warning(f"Unused weights: {unused_weights}"					)




def __lowercase    (							_a	,    _a	,    _a	,    _a	,    _a					):
    snake_case_				:  Tuple       =			full_name.split('''conv_layers.'''					)[-1]
    snake_case_				:  int       =			name.split('''.'''					)
    snake_case_				:  int       =			int(items[0]					)
    snake_case_				:  int       =			int(items[1]					)

    if type_id == 0:
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                raise ValueError(
                    f"{full_name} has size {value.shape}, but"
                    f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."					)
            snake_case_				:  int       =			value
            logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."					)
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                raise ValueError(
                    f"{full_name} has size {value.shape}, but"
                    f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."					)
            snake_case_				:  str       =			value
            logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."					)
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                raise ValueError(
                    f"{full_name} has size {value.shape}, but"
                    f" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."					)
            snake_case_				:  Dict       =			value
            logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."					)
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                raise ValueError(
                    f"{full_name} has size {value.shape}, but"
                    f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."					)
            snake_case_				:  Optional[Any]       =			value
            logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."					)
    else:
        unused_weights.append(_a					)




@torch.no_grad()
def __lowercase    (							_a	,    _a	,    _a=None	,    _a=None	,    _a=True					):
    if config_path is not None:
        snake_case_				:  int       =			UniSpeechSatConfig.from_pretrained(_a					)
    else:
        snake_case_				:  Tuple       =			UniSpeechSatConfig()

    snake_case_				:  List[Any]       =			''''''

    if is_finetuned:
        snake_case_				:  str       =			UniSpeechSatForCTC(_a					)
    else:
        snake_case_				:  Dict       =			UniSpeechSatForPreTraining(_a					)

    snake_case_,			snake_case_,			snake_case_				:  str       =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]	,    arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''					)[:-1]					)}					)
    snake_case_				:  Dict       =			model[0].eval()

    recursively_load_weights(_a	,    _a					)

    hf_wavavec.save_pretrained(_a					)


if __name__ == "__main__":
     lowercase__					:						List[Any]												=			argparse.ArgumentParser()
     parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
     parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
     parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
     parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
     parser.add_argument(
         '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
     )
     lowercase__					:						Optional[int]												=			parser.parse_args()
     convert_unispeech_sat_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )





