

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE					:  List[str]             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  int             =      {
    '''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''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A				,		_A    ):





     '''simple docstring'''




     for attribute in key.split('''.'''    ):
          SCREAMING_SNAKE_CASE__   =   getattr(_A				,		_A    )

     if weight_type is not None:
          SCREAMING_SNAKE_CASE__   =   getattr(_A				,		_A    ).shape
     else:
          SCREAMING_SNAKE_CASE__   =   hf_pointer.shape

     assert hf_shape == value.shape, (
         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":
          SCREAMING_SNAKE_CASE__   =   value
     elif weight_type == "weight_g":
          SCREAMING_SNAKE_CASE__   =   value
     elif weight_type == "weight_v":
          SCREAMING_SNAKE_CASE__   =   value
     elif weight_type == "bias":
          SCREAMING_SNAKE_CASE__   =   value
     else:
          SCREAMING_SNAKE_CASE__   =   value

     logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''    )
def     UpperCAmelCase_       (						_A				,		_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   []
     SCREAMING_SNAKE_CASE__   =   fairseq_model.state_dict()

     SCREAMING_SNAKE_CASE__   =   hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          SCREAMING_SNAKE_CASE__   =   False
          if "conv_layers" in name:
               load_conv_layer(
                   _A				,		_A				,		_A				,		_A				,		hf_model.config.feat_extract_norm == '''group'''				,		)
               SCREAMING_SNAKE_CASE__   =   True
          else:
               for key, mapped_key in MAPPING.items():
                    SCREAMING_SNAKE_CASE__   =   '''hubert.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

                    if key in name or (key.split('''w2v_model.'''    )[-1] == name.split('''.'''    )[0] and not is_finetuned):
                         SCREAMING_SNAKE_CASE__   =   True
                         if "*" in mapped_key:
                              SCREAMING_SNAKE_CASE__   =   name.split(_A    )[0].split('''.'''    )[-2]
                              SCREAMING_SNAKE_CASE__   =   mapped_key.replace('''*'''				,		_A    )
                         if "weight_g" in name:
                              SCREAMING_SNAKE_CASE__   =   '''weight_g'''
                         elif "weight_v" in name:
                              SCREAMING_SNAKE_CASE__   =   '''weight_v'''
                         elif "weight" in name:
                              SCREAMING_SNAKE_CASE__   =   '''weight'''
                         elif "bias" in name:
                              SCREAMING_SNAKE_CASE__   =   '''bias'''
                         else:
                              SCREAMING_SNAKE_CASE__   =   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     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   full_name.split('''conv_layers.'''    )[-1]
     SCREAMING_SNAKE_CASE__   =   name.split('''.'''    )
     SCREAMING_SNAKE_CASE__   =   int(items[0]    )
     SCREAMING_SNAKE_CASE__   =   int(items[1]    )

     if type_id == 0:
          if "bias" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                   F'''{full_name} has size {value.shape}, but'''
                   F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
               )
               SCREAMING_SNAKE_CASE__   =   value
               logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''    )
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                   F'''{full_name} has size {value.shape}, but'''
                   F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
               )
               SCREAMING_SNAKE_CASE__   =   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:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                   F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
                   " found."
               )
               SCREAMING_SNAKE_CASE__   =   value
               logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''    )
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                   F'''{full_name} has size {value.shape}, but'''
                   F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
               )
               SCREAMING_SNAKE_CASE__   =   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     UpperCAmelCase_       (						_A				,		_A				,		_A=None				,		_A=None				,		_A=True    ):





     '''simple docstring'''




     if config_path is not None:
          SCREAMING_SNAKE_CASE__   =   HubertConfig.from_pretrained(_A    )
     else:
          SCREAMING_SNAKE_CASE__   =   HubertConfig()

     if is_finetuned:
          if dict_path:
               SCREAMING_SNAKE_CASE__   =   Dictionary.load(_A    )

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               SCREAMING_SNAKE_CASE__   =   target_dict.pad_index
               SCREAMING_SNAKE_CASE__   =   target_dict.bos_index
               SCREAMING_SNAKE_CASE__   =   target_dict.eos_index
               SCREAMING_SNAKE_CASE__   =   len(target_dict.symbols    )
               SCREAMING_SNAKE_CASE__   =   os.path.join(_A				,		'''vocab.json'''    )
               if not os.path.isdir(_A    ):
                    logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_A    )    )
                    return
               os.makedirs(_A				,		exist_ok=_A    )
               with open(_A				,		'''w'''				,		encoding='''utf-8'''    ) as vocab_handle:
                    json.dump(target_dict.indices				,		_A    )
               SCREAMING_SNAKE_CASE__   =   WavaVecaCTCTokenizer(
                   _A				,		unk_token=target_dict.unk_word				,		pad_token=target_dict.pad_word				,		bos_token=target_dict.bos_word				,		eos_token=target_dict.eos_word				,		word_delimiter_token='''|'''				,		do_lower_case=_A				,		)
               SCREAMING_SNAKE_CASE__   =   True if config.feat_extract_norm == '''layer''' else False
               SCREAMING_SNAKE_CASE__   =   WavaVecaFeatureExtractor(
                   feature_size=1				,		sampling_rate=1_60_00				,		padding_value=0				,		do_normalize=_A				,		return_attention_mask=_A				,		)
               SCREAMING_SNAKE_CASE__   =   WavaVecaProcessor(feature_extractor=_A				,		tokenizer=_A    )
               processor.save_pretrained(_A    )

          SCREAMING_SNAKE_CASE__   =   HubertForCTC(_A    )
     else:
          SCREAMING_SNAKE_CASE__   =   HubertModel(_A    )

     if is_finetuned:
          SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]				,		arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''    )[:-1]    )}    )
     else:
          SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    )

     SCREAMING_SNAKE_CASE__   =   model[0].eval()

     recursively_load_weights(_A				,		_A				,		_A    )

     hf_wavavec.save_pretrained(_A    )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE					:  Union[str, 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'''
       )
       _SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      parser.parse_args()
       convert_hubert_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )





import argparse
import copy
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   {}

     with open(_A    ) as f:
          for line in f:
               if line.split()[0] not in dict_of_neighbours:
                    SCREAMING_SNAKE_CASE__   =   []
                    _list.append([line.split()[1], line.split()[2]]    )
                    SCREAMING_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:
                    SCREAMING_SNAKE_CASE__   =   []
                    _list.append([line.split()[0], line.split()[2]]    )
                    SCREAMING_SNAKE_CASE__   =   _list
               else:
                    dict_of_neighbours[line.split()[1]].append(
                        [line.split()[0], line.split()[2]]    )

     return dict_of_neighbours
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     with open(_A    ) as f:
          SCREAMING_SNAKE_CASE__   =   f.read(1    )
     SCREAMING_SNAKE_CASE__   =   start_node

     SCREAMING_SNAKE_CASE__   =   []

     SCREAMING_SNAKE_CASE__   =   start_node

     SCREAMING_SNAKE_CASE__   =   0
     while visiting not in first_solution:
          SCREAMING_SNAKE_CASE__   =   1_00_00
          for k in dict_of_neighbours[visiting]:
               if int(k[1]    ) < int(_A    ) and k[0] not in first_solution:
                    SCREAMING_SNAKE_CASE__   =   k[1]
                    SCREAMING_SNAKE_CASE__   =   k[0]

          first_solution.append(_A    )
          SCREAMING_SNAKE_CASE__   =   distance_of_first_solution + int(_A    )
          SCREAMING_SNAKE_CASE__   =   best_node

     first_solution.append(_A    )

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

     SCREAMING_SNAKE_CASE__   =   (
         distance_of_first_solution
         + int(dict_of_neighbours[first_solution[-2]][position][1]    )
         - 1_00_00
     )
     return first_solution, distance_of_first_solution
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   []

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

               SCREAMING_SNAKE_CASE__   =   copy.deepcopy(_A    )
               SCREAMING_SNAKE_CASE__   =   kn
               SCREAMING_SNAKE_CASE__   =   n

               SCREAMING_SNAKE_CASE__   =   0

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

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

     SCREAMING_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				,		_A				,		_A				,		_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   1
     SCREAMING_SNAKE_CASE__   =   first_solution
     SCREAMING_SNAKE_CASE__   =   []
     SCREAMING_SNAKE_CASE__   =   distance_of_first_solution
     SCREAMING_SNAKE_CASE__   =   solution

     while count <= iters:
          SCREAMING_SNAKE_CASE__   =   find_neighborhood(_A				,		_A    )
          SCREAMING_SNAKE_CASE__   =   0
          SCREAMING_SNAKE_CASE__   =   neighborhood[index_of_best_solution]
          SCREAMING_SNAKE_CASE__   =   len(_A    ) - 1

          SCREAMING_SNAKE_CASE__   =   False
          while not found:
               SCREAMING_SNAKE_CASE__   =   0
               while i < len(_A    ):
                    if best_solution[i] != solution[i]:
                         SCREAMING_SNAKE_CASE__   =   best_solution[i]
                         SCREAMING_SNAKE_CASE__   =   solution[i]
                         break
                    SCREAMING_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]    )
                    SCREAMING_SNAKE_CASE__   =   True
                    SCREAMING_SNAKE_CASE__   =   best_solution[:-1]
                    SCREAMING_SNAKE_CASE__   =   neighborhood[index_of_best_solution][best_cost_index]
                    if cost < best_cost:
                         SCREAMING_SNAKE_CASE__   =   cost
                         SCREAMING_SNAKE_CASE__   =   solution
               else:
                    SCREAMING_SNAKE_CASE__   =   index_of_best_solution + 1
                    SCREAMING_SNAKE_CASE__   =   neighborhood[index_of_best_solution]

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

          SCREAMING_SNAKE_CASE__   =   count + 1

     return best_solution_ever, best_cost
def     UpperCAmelCase_       (						_A=None    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   generate_neighbours(args.File    )

     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   generate_first_solution(
         args.File				,		_A    )

     SCREAMING_SNAKE_CASE__,SCREAMING_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__":
       _SCREAMING_SNAKE_CASE					:  Tuple             =      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())


