


"""simple docstring"""







import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
__lowercase    =				logging.get_logger(__name__)

__lowercase    =				{
    "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    =				[
    "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_	)->				int:
  '''simple docstring'''







  for attribute in key.split("."	):
    a						:		int											=      getattr(lowerCAmelCase__  ,       lowerCAmelCase__	)

  if weight_type is not None:
    a						:		List[Any]											=      getattr(lowerCAmelCase__  ,       lowerCAmelCase__	).shape
  else:
    a						:		List[str]											=      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":
    a						:		Dict											=      value
  elif weight_type == "weight_g":
    a						:		Optional[int]											=      value
  elif weight_type == "weight_v":
    a						:		Union[str, Any]											=      value
  elif weight_type == "bias":
    a						:		Any											=      value
  else:
    a						:		str											=      value

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







  a						:		List[str]											=      []
  a						:		Union[str, Any]											=      fairseq_model.state_dict()

  a						:		int											=      hf_model.unispeech_sat.feature_extractor

  for name, value in fairseq_dict.items():
    a						:		Any											=      False
    if "conv_layers" in name:
      load_conv_layer(
          lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       hf_model.config.feat_extract_norm == "group"  ,       )
      a						:		Optional[Any]											=      True
    else:
      for key, mapped_key in MAPPING.items():
        a						:		Union[str, Any]											=      "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
          a						:		Any											=      True
          if "*" in mapped_key:
            a						:		Union[str, Any]											=      name.split(lowerCAmelCase__	)[0].split("."	)[-2]
            a						:		Union[str, Any]											=      mapped_key.replace("*"  ,       lowerCAmelCase__	)
          if "weight_g" in name:
            a						:		int											=      "weight_g"
          elif "weight_v" in name:
            a						:		Dict											=      "weight_v"
          elif "bias" in name:
            a						:		Dict											=      "bias"
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            a						:		List[Any]											=      "weight"
          else:
            a						:		str											=      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 					lowercase      (    A_  ,       A_  ,       A_  ,       A_  ,       A_	)->				List[Any]:
  '''simple docstring'''







  a						:		Any											=      full_name.split("conv_layers."	)[-1]
  a						:		Tuple											=      name.split("."	)
  a						:		Optional[int]											=      int(items[0]	)
  a						:		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.'''	)
      a						:		str											=      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.'''	)
      a						:		List[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.'''	)
      a						:		Optional[Any]											=      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.'''	)
      a						:		Optional[int]											=      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 					lowercase      (    A_  ,       A_  ,       A_=None  ,       A_=None  ,       A_=True	)->				Any:
  '''simple docstring'''







  if config_path is not None:
    a						:		List[str]											=      UniSpeechSatConfig.from_pretrained(lowerCAmelCase__	)
  else:
    a						:		Optional[Any]											=      UniSpeechSatConfig()

  a						:		int											=      ""

  if is_finetuned:
    a						:		int											=      UniSpeechSatForCTC(lowerCAmelCase__	)
  else:
    a						:		Optional[Any]											=      UniSpeechSatForPreTraining(lowerCAmelCase__	)

  a							,		a							,		a						:		Optional[int]											=      fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]  ,       arg_overrides={"data": "/".join(dict_path.split("/"	)[:-1]	)}	)
  a						:		Any											=      model[0].eval()

  recursively_load_weights(lowerCAmelCase__  ,       lowerCAmelCase__	)

  hf_wavavec.save_pretrained(lowerCAmelCase__	)


if __name__ == "__main__":
 __lowercase    =				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    =				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
 )










"""simple docstring"""







def 					lowercase      (    A_  ,       A_	)->				float:
  '''simple docstring'''







  def get_matched_characters(A_  ,       A_	) -> str:
    a						:		Optional[int]											=      []
    a						:		List[Any]											=      min(len(_stra	)  ,       len(_stra	)	) // 2
    for i, l in enumerate(_stra	):
      a						:		int											=      int(max(0  ,       i - limit	)	)
      a						:		Optional[int]											=      int(min(i + limit + 1  ,       len(_stra	)	)	)
      if l in _stra[left:right]:
        matched.append(A_	)
        a						:		int											=      F'''{_stra[0:_stra.index(A_	)]} {_stra[_stra.index(A_	) + 1:]}'''

    return "".join(A_	)

  # matching characters
  a						:		Tuple											=      get_matched_characters(A_  ,       A_	)
  a						:		str											=      get_matched_characters(A_  ,       A_	)
  a						:		List[str]											=      len(A_	)

  # transposition
  a						:		Union[str, Any]											=      (
      len([(ca, ca) for ca, ca in zip(A_  ,       A_	) if ca != ca]	) // 2
  )

  if not match_count:
    a						:		Tuple											=      0.0
  else:
    a						:		List[str]											=      (
        1
        / 3
        * (
            match_count / len(A_	)
            + match_count / len(A_	)
            + (match_count - transpositions) / match_count
        )
    )

  # common prefix up to 4 characters
  a						:		Union[str, Any]											=      0
  for ca, ca in zip(stra[:4]  ,       stra[:4]	):
    if ca == ca:
      prefix_len += 1
    else:
      break

  return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(jaro_winkler("""hello""", """world"""))






