"""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 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Tuple:
  for attribute in key.split('.'   ):
    a__:     List[str]										=	getattr(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

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

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







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Optional[Any]:
  a__:     int										=	[]
  a__:     Optional[Any]										=	fairseq_model.state_dict()

  a__:     str										=	hf_model.unispeech_sat.feature_extractor

  for name, value in fairseq_dict.items():
    a__:     str										=	False
    if "conv_layers" in name:
      load_conv_layer(
          _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     hf_model.config.feat_extract_norm == 'group'		,     )
      a__:     int										=	True
    else:
      for key, mapped_key in MAPPING.items():
        a__:     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
          a__:     Dict										=	True
          if "*" in mapped_key:
            a__:     Union[str, Any]										=	name.split(_SCREAMING_SNAKE_CASE   )[0].split('.'   )[-2]
            a__:     Optional[int]										=	mapped_key.replace('*'		,     _SCREAMING_SNAKE_CASE   )
          if "weight_g" in name:
            a__:     List[str]										=	'weight_g'
          elif "weight_v" in name:
            a__:     List[Any]										=	'weight_v'
          elif "bias" in name:
            a__:     Any										=	'bias'
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            a__:     List[Any]										=	'weight'
          else:
            a__:     List[Any]										=	None
          set_recursively(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
        continue
    if not is_used:
      unused_weights.append(_SCREAMING_SNAKE_CASE   )

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







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Dict:
  a__:     int										=	full_name.split('conv_layers.'   )[-1]
  a__:     List[str]										=	name.split('.'   )
  a__:     List[Any]										=	int(items[0]   )
  a__:     Optional[Any]										=	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__:     List[Any]										=	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__:     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.'   )
      a__:     Union[str, 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__:     List[Any]										=	value
      logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'   )
  else:
    unused_weights.append(_SCREAMING_SNAKE_CASE   )







@torch.no_grad()
def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=None		,     _SCREAMING_SNAKE_CASE=None		,     _SCREAMING_SNAKE_CASE=True   )	->Optional[int]:
  if config_path is not None:
    a__:     int										=	UniSpeechSatConfig.from_pretrained(_SCREAMING_SNAKE_CASE   )
  else:
    a__:     Dict										=	UniSpeechSatConfig()

  a__:     Any										=	''

  if is_finetuned:
    a__:     str										=	UniSpeechSatForCTC(_SCREAMING_SNAKE_CASE   )
  else:
    a__:     Union[str, Any]										=	UniSpeechSatForPreTraining(_SCREAMING_SNAKE_CASE   )

  a__							,    a__							,    a__:     List[str]										=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]		,     arg_overrides={'data': '/'.join(dict_path.split('/'   )[:-1]   )}   )
  a__:     int										=	model[0].eval()

  recursively_load_weights(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

  hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE   )


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 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->int:
  while a != 0:
    a__							,    a__:     List[str]										=	b % a, a
  return b







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->int:
  if gcd(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ) != 1:
    a__:     Dict										=	F'mod inverse of {a!r} and {m!r} does not exist'
    raise ValueError(_SCREAMING_SNAKE_CASE   )
  a__							,    a__							,    a__:     Union[str, Any]										=	1, 0, a
  a__							,    a__							,    a__:     Any										=	0, 1, m
  while va != 0:
    a__:     int										=	ua // va
    a__							,    a__							,    a__							,    a__							,    a__							,    a__:     Any										=	(ua - q * va), (ua - q * va), (ua - q * va), va, va, va
  return ua % m


