





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

A__				:   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__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      for attribute in key.split("""."""  ):
            _lowerCAmelCase							     =							getattr(_lowercase							,	_lowercase  )

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

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





      """simple docstring"""


      _lowerCAmelCase							     =							[]
      _lowerCAmelCase							     =							fairseq_model.state_dict()

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

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

      logger.warning(f"Unused weights: {unused_weights}"  )
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase							     =							full_name.split("""conv_layers."""  )[-1]
      _lowerCAmelCase							     =							name.split("""."""  )
      _lowerCAmelCase							     =							int(items[0]  )
      _lowerCAmelCase							     =							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."
                  )
                  _lowerCAmelCase							     =							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."
                  )
                  _lowerCAmelCase							     =							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."
                  )
                  _lowerCAmelCase							     =							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."
                  )
                  _lowerCAmelCase							     =							value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."  )
      else:
            unused_weights.append(_lowercase  )
@torch.no_grad()
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase=None							,	lowerCAmelCase=None							,	lowerCAmelCase=True  ):





      """simple docstring"""


      if config_path is not None:
            _lowerCAmelCase							     =							HubertConfig.from_pretrained(_lowercase  )
      else:
            _lowerCAmelCase							     =							HubertConfig()

      if is_finetuned:
            if dict_path:
                  _lowerCAmelCase							     =							Dictionary.load(_lowercase  )

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

            _lowerCAmelCase							     =							HubertForCTC(_lowercase  )
      else:
            _lowerCAmelCase							     =							HubertModel(_lowercase  )

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

      _lowerCAmelCase							     =							model[0].eval()

      recursively_load_weights(_lowercase							,	_lowercase							,	_lowercase  )

      hf_wavavec.save_pretrained(_lowercase  )


if __name__ == "__main__":
      A__				:   Tuple      =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'''
      )
      A__				:   Optional[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
      )













"""simple docstring"""




def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list					) ->int:






	'''simple docstring'''






	if not grid or not grid[0]:
		raise TypeError("The grid does not contain the appropriate information"					)

	for cell_n in range(1    ,       len(grid[0]					)					):
		grid[0][cell_n] += grid[0][cell_n - 1]
	a      :		Union[str, Any]	=				grid[0]

	for row_n in range(1    ,       len(_lowercase					)					):
		a      :		Optional[Any]	=				grid[row_n]
		a      :		Tuple	=				fill_row(_lowercase    ,       _lowercase					)
		a      :		List[Any]	=				grid[row_n]

	return grid[-1][-1]






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list    ,       _lowercase						:    list					) ->list:






	'''simple docstring'''






	current_row[0] += row_above[0]
	for cell_n in range(1    ,       len(_lowercase					)					):
		current_row[cell_n] += min(current_row[cell_n - 1]    ,       row_above[cell_n]					)

	return current_row


if __name__ == "__main__":
		import doctest

		doctest.testmod()


