

'''simple docstring'''







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()
__lowerCamelCase		       =    logging.get_logger(__name__)

__lowerCamelCase		       =    {
    '''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__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			List[Any]:
       for attribute in key.split("""."""				):
              A_						=      getattr(UpperCAmelCase__,       UpperCAmelCase__				)

       if weight_type is not None:
              A_						=      getattr(UpperCAmelCase__,       UpperCAmelCase__				).shape
       else:
              A_						=      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":
              A_						=      value
       elif weight_type == "weight_g":
              A_						=      value
       elif weight_type == "weight_v":
              A_						=      value
       elif weight_type == "bias":
              A_						=      value
       else:
              A_						=      value

       logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''				)
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			Optional[int]:
       A_						=      []
       A_						=      fairseq_model.state_dict()

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

       for name, value in fairseq_dict.items():
              A_						=      False
              if "conv_layers" in name:
                     load_conv_layer(
                         UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       hf_model.config.feat_extract_norm == """group""",       )
                     A_						=      True
              else:
                     for key, mapped_key in MAPPING.items():
                            A_						=      """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):
                                   A_						=      True
                                   if "*" in mapped_key:
                                          A_						=      name.split(UpperCAmelCase__				)[0].split("""."""				)[-2]
                                          A_						=      mapped_key.replace("""*""",       UpperCAmelCase__				)
                                   if "weight_g" in name:
                                          A_						=      """weight_g"""
                                   elif "weight_v" in name:
                                          A_						=      """weight_v"""
                                   elif "weight" in name:
                                          A_						=      """weight"""
                                   elif "bias" in name:
                                          A_						=      """bias"""
                                   else:
                                          A_						=      None
                                   set_recursively(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)
                            continue
              if not is_used:
                     unused_weights.append(UpperCAmelCase__				)

       logger.warning(F'''Unused weights: {unused_weights}'''				)
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			Any:
       A_						=      full_name.split("""conv_layers."""				)[-1]
       A_						=      name.split("""."""				)
       A_						=      int(items[0]				)
       A_						=      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.'''
                     )
                     A_						=      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.'''
                     )
                     A_						=      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."
                     )
                     A_						=      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.'''
                     )
                     A_						=      value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
       else:
              unused_weights.append(UpperCAmelCase__				)
@torch.no_grad()
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__=None,       UpperCAmelCase__=None,       UpperCAmelCase__=True				)			->			List[Any]:
       if config_path is not None:
              A_						=      HubertConfig.from_pretrained(UpperCAmelCase__				)
       else:
              A_						=      HubertConfig()

       if is_finetuned:
              if dict_path:
                     A_						=      Dictionary.load(UpperCAmelCase__				)

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

              A_						=      HubertForCTC(UpperCAmelCase__				)
       else:
              A_						=      HubertModel(UpperCAmelCase__				)

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

       A_						=      model[0].eval()

       recursively_load_weights(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

       hf_wavavec.save_pretrained(UpperCAmelCase__				)


if __name__ == "__main__":
      __lowerCamelCase		       =    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'''
      )
      __lowerCamelCase		       =    parser.parse_args()
      convert_hubert_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )



'''simple docstring'''







def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__ = 0,       UpperCAmelCase__ = 0				)			->			int:
       A_						=      right or len(UpperCAmelCase__				) - 1
       if left > right:
              return -1
       elif list_data[left] == key:
              return left
       elif list_data[right] == key:
              return right
       else:
              return search(UpperCAmelCase__,       UpperCAmelCase__,       left + 1,       right - 1				)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
