




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
snake_case__ :						Tuple     =			logging.get_logger(__name__)

snake_case__ :						Any     =			{
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''attention.self.query_proj''': '''encoder.encoder.layer.*.attention.self.query_proj''',
    '''attention.self.key_proj''': '''encoder.encoder.layer.*.attention.self.key_proj''',
    '''attention.self.value_proj''': '''encoder.encoder.layer.*.attention.self.value_proj''',
    '''attention.output.dense''': '''encoder.encoder.layer.*.attention.output.dense''',
    '''attention.output.LayerNorm''': '''encoder.encoder.layer.*.attention.output.LayerNorm''',
    '''intermediate.dense''': '''encoder.encoder.layer.*.intermediate.dense''',
    '''output.dense''': '''encoder.encoder.layer.*.output.dense''',
    '''output.LayerNorm''': '''encoder.encoder.layer.*.output.LayerNorm''',
    '''encoder.encoder.rel_embeddings''': '''encoder.encoder.rel_embeddings''',
    '''encoder.encoder.LayerNorm''': '''encoder.encoder.LayerNorm''',
    '''encoder.upsample.0''': '''encoder.upsample.projection''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}


def 						lowercase		(     _lowerCAmelCase   ,     _lowerCAmelCase   ,     _lowerCAmelCase   ,     _lowerCAmelCase   ,     _lowerCAmelCase      ):
  for attribute in key.split("""."""      ):
    UpperCAmelCase__    						=   getattr(_lowerCAmelCase   ,     _lowerCAmelCase      )

  if weight_type is not None:
    UpperCAmelCase__    						=   getattr(_lowerCAmelCase   ,     _lowerCAmelCase      ).shape
  else:
    UpperCAmelCase__    						=   hf_pointer.shape

  assert hf_shape == value.shape, (
      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":
    UpperCAmelCase__    						=   value
  elif weight_type == "weight_g":
    UpperCAmelCase__    						=   value
  elif weight_type == "weight_v":
    UpperCAmelCase__    						=   value
  elif weight_type == "bias":
    UpperCAmelCase__    						=   value
  else:
    UpperCAmelCase__    						=   value

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


def 						lowercase		(     _lowerCAmelCase   ,     _lowerCAmelCase   ,     _lowerCAmelCase      ):
  UpperCAmelCase__    						=   []
  UpperCAmelCase__    						=   fairseq_model.state_dict()

  UpperCAmelCase__    						=   hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    UpperCAmelCase__    						=   False
    if "conv_layers" in name:
      load_conv_layer(
          _lowerCAmelCase   ,     _lowerCAmelCase   ,     _lowerCAmelCase   ,     _lowerCAmelCase   ,     hf_model.config.feat_extract_norm == """group"""   ,     )
      UpperCAmelCase__    						=   True
    else:
      for key, mapped_key in MAPPING.items():
        UpperCAmelCase__    						=   """sew_d.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

        if key in name or key.split("""w2v_model."""      )[-1] == name.split("""."""      )[0]:
          UpperCAmelCase__    						=   True
          if "*" in mapped_key:
            UpperCAmelCase__    						=   name.split(_lowerCAmelCase      )[0].split("""."""      )[-2]
            if not layer_index.isnumeric():
              continue
            UpperCAmelCase__    						=   mapped_key.replace("""*"""   ,     _lowerCAmelCase      )
          if "weight_g" in name:
            UpperCAmelCase__    						=   """weight_g"""
          elif "weight_v" in name:
            UpperCAmelCase__    						=   """weight_v"""
          elif "weight" in name:
            UpperCAmelCase__    						=   """weight"""
          elif "bias" in name:
            UpperCAmelCase__    						=   """bias"""
          else:
            UpperCAmelCase__    						=   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		(     _lowerCAmelCase   ,     _lowerCAmelCase   ,     _lowerCAmelCase   ,     _lowerCAmelCase   ,     _lowerCAmelCase      ):
  UpperCAmelCase__    						=   full_name.split("""conv_layers."""      )[-1]
  UpperCAmelCase__    						=   name.split("""."""      )
  UpperCAmelCase__    						=   int(items[0]      )
  UpperCAmelCase__    						=   int(items[1]      )

  if type_id == 0:
    if "bias" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
          F'''{full_name} has size {value.shape}, but'''
          F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
      )
      UpperCAmelCase__    						=   value
      logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''      )
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
          F'''{full_name} has size {value.shape}, but'''
          F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
      )
      UpperCAmelCase__    						=   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:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
          F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
          " found."
      )
      UpperCAmelCase__    						=   value
      logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
          F'''{full_name} has size {value.shape}, but'''
          F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
      )
      UpperCAmelCase__    						=   value
      logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
  else:
    unused_weights.append(_lowerCAmelCase      )


def 						lowercase		(     _lowerCAmelCase   ,     _lowerCAmelCase      ):
  UpperCAmelCase__    						=   SEWDConfig()
  if is_finetuned:
    UpperCAmelCase__    						=   model.wav_encoder.wav_model.cfg
  else:
    UpperCAmelCase__    						=   model.cfg

  UpperCAmelCase__    						=   fs_config.conv_bias
  UpperCAmelCase__    						=   eval(fs_config.conv_feature_layers      )
  UpperCAmelCase__    						=   [x[0] for x in conv_layers]
  UpperCAmelCase__    						=   [x[1] for x in conv_layers]
  UpperCAmelCase__    						=   [x[2] for x in conv_layers]
  UpperCAmelCase__    						=   """gelu"""
  UpperCAmelCase__    						=   """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
  UpperCAmelCase__    						=   0.0
  UpperCAmelCase__    						=   fs_config.activation_fn.name
  UpperCAmelCase__    						=   fs_config.encoder_embed_dim
  UpperCAmelCase__    						=   0.02
  UpperCAmelCase__    						=   fs_config.encoder_ffn_embed_dim
  UpperCAmelCase__    						=   1e-5
  UpperCAmelCase__    						=   fs_config.encoder_layerdrop
  UpperCAmelCase__    						=   fs_config.encoder_attention_heads
  UpperCAmelCase__    						=   fs_config.conv_pos_groups
  UpperCAmelCase__    						=   fs_config.conv_pos
  UpperCAmelCase__    						=   len(_lowerCAmelCase      )
  UpperCAmelCase__    						=   fs_config.encoder_layers
  UpperCAmelCase__    						=   fs_config.squeeze_factor
  # DeBERTa-specific parameters:
  UpperCAmelCase__    						=   fs_config.max_position_embeddings
  UpperCAmelCase__    						=   fs_config.position_buckets
  UpperCAmelCase__    						=   fs_config.share_att_key
  UpperCAmelCase__    						=   fs_config.relative_attention
  UpperCAmelCase__    						=   fs_config.position_biased_input
  UpperCAmelCase__    						=   tuple(fs_config.pos_att_type.split("""|"""      )      )
  UpperCAmelCase__    						=   fs_config.norm_rel_ebd

  # take care of any params that are overridden by the Wav2VecCtc model
  if is_finetuned:
    UpperCAmelCase__    						=   model.cfg
    UpperCAmelCase__    						=   fs_config.final_dropout
    UpperCAmelCase__    						=   fs_config.layerdrop
  UpperCAmelCase__    						=   fs_config.activation_dropout
  UpperCAmelCase__    						=   fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
  UpperCAmelCase__    						=   fs_config.attention_dropout
  UpperCAmelCase__    						=   fs_config.dropout_input
  UpperCAmelCase__    						=   fs_config.dropout
  UpperCAmelCase__    						=   fs_config.mask_channel_length
  UpperCAmelCase__    						=   fs_config.mask_channel_prob
  UpperCAmelCase__    						=   fs_config.mask_length
  UpperCAmelCase__    						=   fs_config.mask_prob

  UpperCAmelCase__    						=   """Wav2Vec2FeatureExtractor"""
  UpperCAmelCase__    						=   """Wav2Vec2CTCTokenizer"""

  return config


@torch.no_grad()
def 						lowercase		(     _lowerCAmelCase   ,     _lowerCAmelCase   ,     _lowerCAmelCase=None   ,     _lowerCAmelCase=None   ,     _lowerCAmelCase=True      ):

  if is_finetuned:
    UpperCAmelCase__						,					UpperCAmelCase__						,					UpperCAmelCase__    						=   fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]   ,     arg_overrides={"""data""": """/""".join(dict_path.split("""/"""      )[:-1]      )}      )
  else:
    UpperCAmelCase__						,					UpperCAmelCase__						,					UpperCAmelCase__    						=   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      )

  if config_path is not None:
    UpperCAmelCase__    						=   SEWDConfig.from_pretrained(_lowerCAmelCase      )
  else:
    UpperCAmelCase__    						=   convert_config(model[0]   ,     _lowerCAmelCase      )
  UpperCAmelCase__    						=   model[0].eval()

  UpperCAmelCase__    						=   True if config.feat_extract_norm == """layer""" else False
  UpperCAmelCase__    						=   WavaVecaFeatureExtractor(
      feature_size=1   ,     sampling_rate=1_6000   ,     padding_value=0   ,     do_normalize=_lowerCAmelCase   ,     return_attention_mask=_lowerCAmelCase   ,     )

  if is_finetuned:
    if dict_path:
      UpperCAmelCase__    						=   Dictionary.load(_lowerCAmelCase      )

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      UpperCAmelCase__    						=   target_dict.pad_index
      UpperCAmelCase__    						=   target_dict.bos_index
      UpperCAmelCase__    						=   target_dict.pad_index
      UpperCAmelCase__    						=   target_dict.bos_index
      UpperCAmelCase__    						=   target_dict.eos_index
      UpperCAmelCase__    						=   len(target_dict.symbols      )
      UpperCAmelCase__    						=   os.path.join(_lowerCAmelCase   ,     """vocab.json"""      )
      if not os.path.isdir(_lowerCAmelCase      ):
        logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_lowerCAmelCase      )      )
        return
      os.makedirs(_lowerCAmelCase   ,     exist_ok=_lowerCAmelCase      )
      with open(_lowerCAmelCase   ,     """w"""   ,     encoding="""utf-8"""      ) as vocab_handle:
        json.dump(target_dict.indices   ,     _lowerCAmelCase      )
      UpperCAmelCase__    						=   WavaVecaCTCTokenizer(
          _lowerCAmelCase   ,     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=_lowerCAmelCase   ,     )
      UpperCAmelCase__    						=   WavaVecaProcessor(feature_extractor=_lowerCAmelCase   ,     tokenizer=_lowerCAmelCase      )
      processor.save_pretrained(_lowerCAmelCase      )

    UpperCAmelCase__    						=   SEWDForCTC(_lowerCAmelCase      )
  else:
    UpperCAmelCase__    						=   SEWDModel(_lowerCAmelCase      )
    feature_extractor.save_pretrained(_lowerCAmelCase      )

  recursively_load_weights(_lowerCAmelCase   ,     _lowerCAmelCase   ,     _lowerCAmelCase      )

  hf_model.save_pretrained(_lowerCAmelCase      )


if __name__ == "__main__":
   snake_case__ :						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(
       '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
   )
   snake_case__ :						Optional[int]     =			parser.parse_args()
   convert_sew_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
   )










def 						lowercase		(     _lowerCAmelCase      ):
  UpperCAmelCase__    						=   5381
  for x in s:
    UpperCAmelCase__    						=   ((hash_value << 5) + hash_value) + ord(_lowerCAmelCase      )
  return hash_value & 0xffffffff




