"""simple docstring"""




import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

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

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

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





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

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

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

  _lowerCAmelCase						:	Tuple        =					""""""

  if is_finetuned:
    _lowerCAmelCase						:	List[Any]        =					UniSpeechSatForCTC(_lowerCamelCase     )
  else:
    _lowerCAmelCase						:	Dict        =					UniSpeechSatForPreTraining(_lowerCamelCase     )

  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	str        =					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				:		Union[str, 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				:		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"""




from __future__ import annotations





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	list     )	->				float:
  if not nums:
    raise ValueError("""List is empty"""     )
  return sum(_lowerCamelCase     ) / len(_lowerCamelCase     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()




