




"""simple docstring"""


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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

A_      =    {
    '''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''',
    '''adapter_layer''': '''encoder.layers.*.adapter_layer''',
    '''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''',
    '''mask_emb''': '''masked_spec_embed''',
    '''pooling_layer.linear''': '''projector''',
    '''pooling_layer.projection''': '''classifier''',
}
A_      =    [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]


def  UpperCAmelCase__	(snake_case__							: List[Any]   ):
  """simple docstring"""

  _snake_case							:		Tuple								=  {}
  with open(snake_case__		,       """r"""   ) as file:
    for line_number, line in enumerate(snake_case__   ):
      _snake_case							:		Optional[Any]								=  line.strip()
      if line:
        _snake_case							:		Tuple								=  line.split()
        _snake_case							:		List[Any]								=  line_number
        _snake_case							:		Dict								=  words[0]
        _snake_case							:		List[Any]								=  value
  return result


def  UpperCAmelCase__	(snake_case__							: Optional[int]		,       snake_case__							: str		,       snake_case__							: Optional[Any]		,       snake_case__							: Dict		,       snake_case__							: List[Any]   ):
  """simple docstring"""

  for attribute in key.split("""."""   ):
    _snake_case							:		List[str]								=  getattr(snake_case__		,       snake_case__   )

  _snake_case							:		List[str]								=  None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(snake_case__   ):
      _snake_case							:		List[Any]								=  PARAM_MAPPING[full_name.split("""."""   )[-1]]
      _snake_case							:		int								=  """param"""

  if weight_type is not None and weight_type != "param":
    _snake_case							:		Optional[int]								=  getattr(snake_case__		,       snake_case__   ).shape
  elif weight_type is not None and weight_type == "param":
    _snake_case							:		Union[str, Any]								=  hf_pointer
    for attribute in hf_param_name.split("""."""   ):
      _snake_case							:		List[Any]								=  getattr(snake_case__		,       snake_case__   )
    _snake_case							:		Optional[Any]								=  shape_pointer.shape

    # let's reduce dimension
    _snake_case							:		Tuple								=  value[0]
  else:
    _snake_case							:		Any								=  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":
    _snake_case							:		Union[str, Any]								=  value
  elif weight_type == "weight_g":
    _snake_case							:		Optional[Any]								=  value
  elif weight_type == "weight_v":
    _snake_case							:		str								=  value
  elif weight_type == "bias":
    _snake_case							:		Union[str, Any]								=  value
  elif weight_type == "param":
    for attribute in hf_param_name.split("""."""   ):
      _snake_case							:		List[Any]								=  getattr(snake_case__		,       snake_case__   )
    _snake_case							:		int								=  value
  else:
    _snake_case							:		Tuple								=  value

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


def  UpperCAmelCase__	(snake_case__							: Optional[int]		,       snake_case__							: List[str]		,       snake_case__							: Union[str, Any]		,       snake_case__							: Optional[Any]		,       snake_case__							: Optional[int]   ):
  """simple docstring"""

  _snake_case							:		int								=  None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(snake_case__   ):
      _snake_case							:		int								=  PARAM_MAPPING[full_name.split("""."""   )[-1]]
      _snake_case							:		Optional[int]								=  """param"""

  if weight_type is not None and weight_type != "param":
    _snake_case							:		Dict								=  """.""".join([key, weight_type]   )
  elif weight_type is not None and weight_type == "param":
    _snake_case							:		Any								=  """.""".join([key, hf_param_name]   )
  else:
    _snake_case							:		Any								=  key

  _snake_case							:		Union[str, Any]								=  value if """lm_head""" in full_key else value[0]


A_      =    {
    '''W_a''': '''linear_1.weight''',
    '''W_b''': '''linear_2.weight''',
    '''b_a''': '''linear_1.bias''',
    '''b_b''': '''linear_2.bias''',
    '''ln_W''': '''norm.weight''',
    '''ln_b''': '''norm.bias''',
}


def  UpperCAmelCase__	(snake_case__							: Dict		,       snake_case__							: str		,       snake_case__							: Tuple=None		,       snake_case__							: List[str]=None   ):
  """simple docstring"""

  _snake_case							:		Optional[int]								=  False
  for key, mapped_key in MAPPING.items():
    _snake_case							:		List[str]								=  """wav2vec2.""" + 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]:
      _snake_case							:		Union[str, Any]								=  True
      if "*" in mapped_key:
        _snake_case							:		int								=  name.split(snake_case__   )[0].split("""."""   )[-2]
        _snake_case							:		List[str]								=  mapped_key.replace("""*"""		,       snake_case__   )
      if "weight_g" in name:
        _snake_case							:		List[str]								=  """weight_g"""
      elif "weight_v" in name:
        _snake_case							:		Union[str, Any]								=  """weight_v"""
      elif "bias" in name:
        _snake_case							:		Tuple								=  """bias"""
      elif "weight" in name:
        # TODO: don't match quantizer.weight_proj
        _snake_case							:		List[Any]								=  """weight"""
      else:
        _snake_case							:		List[str]								=  None
      if hf_dict is not None:
        rename_dict(snake_case__		,       snake_case__		,       snake_case__		,       snake_case__		,       snake_case__   )
      else:
        set_recursively(snake_case__		,       snake_case__		,       snake_case__		,       snake_case__		,       snake_case__   )
      return is_used
  return is_used


def  UpperCAmelCase__	(snake_case__							: Union[str, Any]		,       snake_case__							: Optional[int]		,       snake_case__							: Optional[int]   ):
  """simple docstring"""

  _snake_case							:		int								=  []
  _snake_case							:		List[str]								=  fairseq_model.state_dict()

  _snake_case							:		List[Any]								=  hf_model.wavaveca.feature_extractor

  for name, value in fairseq_dict.items():
    _snake_case							:		Any								=  False
    if "conv_layers" in name:
      load_conv_layer(
          snake_case__		,       snake_case__		,       snake_case__		,       snake_case__		,       hf_model.config.feat_extract_norm == """group"""		,       )
      _snake_case							:		Union[str, Any]								=  True
    else:
      _snake_case							:		str								=  load_wavaveca_layer(snake_case__		,       snake_case__		,       snake_case__   )
    if not is_used:
      unused_weights.append(snake_case__   )

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


def  UpperCAmelCase__	(snake_case__							: int		,       snake_case__							: Any		,       snake_case__							: str		,       snake_case__							: List[Any]		,       snake_case__							: Optional[int]   ):
  """simple docstring"""

  _snake_case							:		Dict								=  full_name.split("""conv_layers."""   )[-1]
  _snake_case							:		List[Any]								=  name.split("""."""   )
  _snake_case							:		List[str]								=  int(items[0]   )
  _snake_case							:		Tuple								=  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."   )
      _snake_case							:		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."   )
      _snake_case							:		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.conv_layers[layer_id].layer_norm.bias.data.shape} was found."   )
      _snake_case							:		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.conv_layers[layer_id].layer_norm.weight.data.shape} was found."   )
      _snake_case							:		Any								=  value
      logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."   )
  else:
    unused_weights.append(snake_case__   )


@torch.no_grad()
def  UpperCAmelCase__	(snake_case__							: str		,       snake_case__							: str		,       snake_case__							: str=None		,       snake_case__							: str=None		,       snake_case__							: Tuple=True		,       snake_case__							: List[Any]=False   ):
  """simple docstring"""

  if config_path is not None:
    _snake_case							:		Union[str, Any]								=  WavaVecaConfig.from_pretrained(snake_case__   )
  else:
    _snake_case							:		Union[str, Any]								=  WavaVecaConfig()

  if is_seq_class:
    _snake_case							:		Optional[Any]								=  read_txt_into_dict(snake_case__   )
    _snake_case							:		Optional[Any]								=  idalabel
    _snake_case							:		str								=  WavaVecaForSequenceClassification(snake_case__   )
    _snake_case							:		List[str]								=  WavaVecaFeatureExtractor(
        feature_size=1		,       sampling_rate=1_60_00		,       padding_value=0		,       do_normalize=snake_case__		,       return_attention_mask=snake_case__		,       )
    feature_extractor.save_pretrained(snake_case__   )

  elif is_finetuned:
    if dict_path:
      _snake_case							:		Union[str, Any]								=  Dictionary.load(snake_case__   )

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      _snake_case							:		int								=  target_dict.pad_index
      _snake_case							:		Dict								=  target_dict.bos_index
      _snake_case							:		Optional[int]								=  target_dict.eos_index
      _snake_case							:		Any								=  len(target_dict.symbols   )
      _snake_case							:		Optional[int]								=  os.path.join(snake_case__		,       """vocab.json"""   )
      if not os.path.isdir(snake_case__   ):
        logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(snake_case__   )   )
        return
      os.makedirs(snake_case__		,       exist_ok=snake_case__   )
      _snake_case							:		str								=  target_dict.indices

      # fairseq has the <pad> and <s> switched
      _snake_case							:		Optional[int]								=  0
      _snake_case							:		List[str]								=  1
      with open(snake_case__		,       """w"""		,       encoding="""utf-8"""   ) as vocab_handle:
        json.dump(snake_case__		,       snake_case__   )
      _snake_case							:		List[str]								=  WavaVecaCTCTokenizer(
          snake_case__		,       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=snake_case__		,       )
      _snake_case							:		Optional[int]								=  True if config.feat_extract_norm == """layer""" else False
      _snake_case							:		List[Any]								=  WavaVecaFeatureExtractor(
          feature_size=1		,       sampling_rate=1_60_00		,       padding_value=0		,       do_normalize=snake_case__		,       return_attention_mask=snake_case__		,       )
      _snake_case							:		Optional[int]								=  WavaVecaProcessor(feature_extractor=snake_case__		,       tokenizer=snake_case__   )
      processor.save_pretrained(snake_case__   )

    _snake_case							:		str								=  WavaVecaForCTC(snake_case__   )
  else:
    _snake_case							:		Tuple								=  WavaVecaForPreTraining(snake_case__   )

  if is_finetuned or is_seq_class:
    _snake_case							:		List[str]								=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]		,       arg_overrides={"""data""": """/""".join(dict_path.split("""/"""   )[:-1]   )}   )
  else:
    _snake_case							:		Optional[Any]								=  argparse.Namespace(task="""audio_pretraining"""   )
    _snake_case							:		Tuple								=  fairseq.tasks.setup_task(snake_case__   )

    _snake_case							:		Optional[int]								=  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		,       task=snake_case__   )

  _snake_case							:		Any								=  model[0].eval()

  recursively_load_weights(snake_case__		,       snake_case__		,       not is_finetuned   )

  hf_wavavec.save_pretrained(snake_case__   )


if __name__ == "__main__":
     A_      =    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'''
     )
     parser.add_argument(
         '''--is_seq_class''',
         action='''store_true''',
         help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
     )
     A_      =    parser.parse_args()

     A_      =    not args.not_finetuned and not args.is_seq_class
     convert_wavaveca_checkpoint(
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.dict_path,
         is_finetuned,
         args.is_seq_class,
     )








"""simple docstring"""


def  UpperCAmelCase__	(snake_case__							: Optional[Any]   ):
  """simple docstring"""

  _snake_case							:		Union[str, Any]								=  []
  _snake_case							:		Dict								=  set({"""(""", """[""", """{"""}   )
  _snake_case							:		Union[str, Any]								=  set({""")""", """]""", """}"""}   )
  _snake_case							:		Tuple								=  {"""{""": """}""", """[""": """]""", """(""": """)"""}

  for i in range(len(snake_case__   )   ):
    if s[i] in open_brackets:
      stack.append(s[i]   )

    elif s[i] in closed_brackets and (
        len(snake_case__   ) == 0 or (len(snake_case__   ) > 0 and open_to_closed[stack.pop()] != s[i])
    ):
      return False

  return len(snake_case__   ) == 0


def  UpperCAmelCase__	():
  """simple docstring"""

  _snake_case							:		Any								=  input("""Enter sequence of brackets: """   )
  if is_balanced(snake_case__   ):
    print(snake_case__		,       """is balanced"""   )
  else:
    print(snake_case__		,       """is not balanced"""   )


if __name__ == "__main__":
     main()


