





"""simple docstring"""




import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
_a   :	List[Any] 					=     logging.get_logger(__name__)

_a   :	Optional[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',
    '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',
}
_a   :	Optional[Any] 					=     [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: List[Any]    ,_lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: Dict    ,_lowerCamelCase		: Optional[int]    ,_lowerCamelCase		: Dict			) ->   List[str]:
  for attribute in key.split("""."""			):
    _lowerCAmelCase		: Tuple						    =  getattr(lowerCamelCase_    ,lowerCamelCase_			)

  if weight_type is not None:
    _lowerCAmelCase		: Union[str, Any]						    =  getattr(lowerCamelCase_    ,lowerCamelCase_			).shape
  else:
    _lowerCAmelCase		: Tuple						    =  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":
    _lowerCAmelCase		: Dict						    =  value
  elif weight_type == "weight_g":
    _lowerCAmelCase		: List[str]						    =  value
  elif weight_type == "weight_v":
    _lowerCAmelCase		: Tuple						    =  value
  elif weight_type == "bias":
    _lowerCAmelCase		: List[Any]						    =  value
  else:
    _lowerCAmelCase		: List[Any]						    =  value

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





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Any    ,_lowerCamelCase		: Dict			) ->   Dict:
  _lowerCAmelCase		: Optional[int]						    =  []
  _lowerCAmelCase		: Optional[Any]						    =  fairseq_model.state_dict()

  _lowerCAmelCase		: List[str]						    =  hf_model.unispeech_sat.feature_extractor

  for name, value in fairseq_dict.items():
    _lowerCAmelCase		: Optional[int]						    =  False
    if "conv_layers" in name:
      load_conv_layer(
          lowerCamelCase_    ,lowerCamelCase_    ,lowerCamelCase_    ,lowerCamelCase_    ,hf_model.config.feat_extract_norm == """group"""    ,)
      _lowerCAmelCase		: List[str]						    =  True
    else:
      for key, mapped_key in MAPPING.items():
        _lowerCAmelCase		: str						    =  """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
          _lowerCAmelCase		: int						    =  True
          if "*" in mapped_key:
            _lowerCAmelCase		: Tuple						    =  name.split(lowerCamelCase_			)[0].split("""."""			)[-2]
            _lowerCAmelCase		: int						    =  mapped_key.replace("""*"""    ,lowerCamelCase_			)
          if "weight_g" in name:
            _lowerCAmelCase		: str						    =  """weight_g"""
          elif "weight_v" in name:
            _lowerCAmelCase		: Optional[Any]						    =  """weight_v"""
          elif "bias" in name:
            _lowerCAmelCase		: List[Any]						    =  """bias"""
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            _lowerCAmelCase		: Dict						    =  """weight"""
          else:
            _lowerCAmelCase		: Tuple						    =  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 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Union[str, Any]    ,_lowerCamelCase		: Optional[int]    ,_lowerCamelCase		: Union[str, Any]    ,_lowerCamelCase		: List[str]    ,_lowerCamelCase		: Optional[int]			) ->   Dict:
  _lowerCAmelCase		: Optional[Any]						    =  full_name.split("""conv_layers."""			)[-1]
  _lowerCAmelCase		: List[str]						    =  name.split("""."""			)
  _lowerCAmelCase		: Tuple						    =  int(items[0]			)
  _lowerCAmelCase		: List[str]						    =  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."			)
      _lowerCAmelCase		: 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."			)
      _lowerCAmelCase		: Union[str, Any]						    =  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."			)
      _lowerCAmelCase		: Optional[int]						    =  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."			)
      _lowerCAmelCase		: List[Any]						    =  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 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: int    ,_lowerCamelCase		: int    ,_lowerCamelCase		: str=None    ,_lowerCamelCase		: Optional[Any]=None    ,_lowerCamelCase		: str=True			) ->   List[Any]:
  if config_path is not None:
    _lowerCAmelCase		: Union[str, Any]						    =  UniSpeechSatConfig.from_pretrained(lowerCamelCase_			)
  else:
    _lowerCAmelCase		: Optional[Any]						    =  UniSpeechSatConfig()

  _lowerCAmelCase		: Dict						    =  """"""

  if is_finetuned:
    _lowerCAmelCase		: str						    =  UniSpeechSatForCTC(lowerCamelCase_			)
  else:
    _lowerCAmelCase		: Optional[Any]						    =  UniSpeechSatForPreTraining(lowerCamelCase_			)

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

  recursively_load_weights(lowerCamelCase_    ,lowerCamelCase_			)

  hf_wavavec.save_pretrained(lowerCamelCase_			)


if __name__ == "__main__":
     _a   :	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'
     )
     _a   :	List[Any] 					=     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"""




_a   :	Optional[Any] 					=     '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

_a   :	List[str] 					=     [{'type': 'code', 'content': INSTALL_CONTENT}]
_a   :	str 					=     {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}




