





'''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()
UpperCAmelCase					:		Optional[Any]		=							logging.get_logger(__name__)

UpperCAmelCase					:		Optional[Any]		=							{
    '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    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      for attribute in key.split('''.'''	):
            _snake_case		:	Tuple					     =     getattr(lowerCAmelCase_			,      lowerCAmelCase_	)

      if weight_type is not None:
            _snake_case		:	Tuple					     =     getattr(lowerCAmelCase_			,      lowerCAmelCase_	).shape
      else:
            _snake_case		:	int					     =     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":
            _snake_case		:	List[str]					     =     value
      elif weight_type == "weight_g":
            _snake_case		:	Tuple					     =     value
      elif weight_type == "weight_v":
            _snake_case		:	str					     =     value
      elif weight_type == "bias":
            _snake_case		:	Optional[int]					     =     value
      else:
            _snake_case		:	str					     =     value

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




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	int					     =     []
      _snake_case		:	str					     =     fairseq_model.state_dict()

      _snake_case		:	Tuple					     =     hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	int					     =     full_name.split('''conv_layers.'''	)[-1]
      _snake_case		:	Any					     =     name.split('''.'''	)
      _snake_case		:	str					     =     int(items[0]	)
      _snake_case		:	Dict					     =     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.'''
                  )
                  _snake_case		:	Any					     =     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.'''
                  )
                  _snake_case		:	Tuple					     =     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."
                  )
                  _snake_case		:	List[str]					     =     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.'''
                  )
                  _snake_case		:	List[str]					     =     value
                  logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
      else:
            unused_weights.append(lowerCAmelCase_	)




@torch.no_grad()
def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_=None			,      lowerCAmelCase_=None			,      lowerCAmelCase_=True	):





      """simple docstring"""



      if config_path is not None:
            _snake_case		:	List[Any]					     =     HubertConfig.from_pretrained(lowerCAmelCase_	)
      else:
            _snake_case		:	Dict					     =     HubertConfig()

      if is_finetuned:
            if dict_path:
                  _snake_case		:	Dict					     =     Dictionary.load(lowerCAmelCase_	)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  _snake_case		:	int					     =     target_dict.pad_index
                  _snake_case		:	List[str]					     =     target_dict.bos_index
                  _snake_case		:	Union[str, Any]					     =     target_dict.eos_index
                  _snake_case		:	Tuple					     =     len(target_dict.symbols	)
                  _snake_case		:	Optional[Any]					     =     os.path.join(lowerCAmelCase_			,      '''vocab.json'''	)
                  if not os.path.isdir(lowerCAmelCase_	):
                        logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(lowerCAmelCase_	)	)
                        return
                  os.makedirs(lowerCAmelCase_			,      exist_ok=lowerCAmelCase_	)
                  with open(lowerCAmelCase_			,      '''w'''			,      encoding='''utf-8'''	) as vocab_handle:
                        json.dump(target_dict.indices			,      lowerCAmelCase_	)
                  _snake_case		:	Any					     =     WavaVecaCTCTokenizer(
                      lowerCAmelCase_			,      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=lowerCAmelCase_			,      )
                  _snake_case		:	Dict					     =     True if config.feat_extract_norm == '''layer''' else False
                  _snake_case		:	str					     =     WavaVecaFeatureExtractor(
                      feature_size=1			,      sampling_rate=16_000			,      padding_value=0			,      do_normalize=lowerCAmelCase_			,      return_attention_mask=lowerCAmelCase_			,      )
                  _snake_case		:	int					     =     WavaVecaProcessor(feature_extractor=lowerCAmelCase_			,      tokenizer=lowerCAmelCase_	)
                  processor.save_pretrained(lowerCAmelCase_	)

            _snake_case		:	List[str]					     =     HubertForCTC(lowerCAmelCase_	)
      else:
            _snake_case		:	List[Any]					     =     HubertModel(lowerCAmelCase_	)

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

      _snake_case		:	Union[str, Any]					     =     model[0].eval()

      recursively_load_weights(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	)

      hf_wavavec.save_pretrained(lowerCAmelCase_	)


if __name__ == "__main__":
   UpperCAmelCase					:		str		=							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'
   )
   UpperCAmelCase					:		List[str]		=							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'''

from __future__ import annotations




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_ = None			,      lowerCAmelCase_ = None	):





      """simple docstring"""



      if start is None:
            _snake_case		:	Optional[Any]					     =     0

      if end is None:
            _snake_case		:	Any					     =     len(lowerCAmelCase_	) - 1

      if start >= end:
            return

      _snake_case		:	Optional[Any]					     =     (start + end) // 2

      slowsort(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	)
      slowsort(lowerCAmelCase_			,      mid + 1			,      lowerCAmelCase_	)

      if sequence[end] < sequence[mid]:
            _snake_case     ,							_snake_case		:	int					     =     sequence[mid], sequence[end]

      slowsort(lowerCAmelCase_			,      lowerCAmelCase_			,      end - 1	)


if __name__ == "__main__":
   from doctest import testmod

   testmod()


