

import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

lowerCamelCase     =      {
    '''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''',
    '''mask_emb''': '''masked_spec_embed''',
}




def     UpperCAmelCase__       (    _A   :				Any			,   _A   :				Tuple			,   _A   :				Optional[int]			,   _A   :				List[Any]			,   _A   :				Union[str, Any]							):





  '''simple docstring'''







  for attribute in key.split('''.'''							):
    a__						=getattr(_A			,   _A							)

  if weight_type is not None:
    a__						=getattr(_A			,   _A							).shape
  else:
    a__						=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":
    a__						=value
  elif weight_type == "weight_g":
    a__						=value
  elif weight_type == "weight_v":
    a__						=value
  elif weight_type == "bias":
    a__						=value
  else:
    a__						=value

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




def     UpperCAmelCase__       (    _A   :				Dict			,   _A   :				Optional[int]							):





  '''simple docstring'''







  a__						=[]
  a__						=fairseq_model.state_dict()

  a__						=hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    a__						=False
    if "conv_layers" in name:
      load_conv_layer(
          _A			,   _A			,   _A			,   _A			,   hf_model.config.feat_extract_norm == '''group'''			,   )
      a__						=True
    else:
      for key, mapped_key in MAPPING.items():
        a__						=mapped_key

        if key in name:
          a__						=True
          if "*" in mapped_key:
            a__						=name.split(_A							)[0].split('''.'''							)[-2]
            a__						=mapped_key.replace('''*'''			,   _A							)
          if "weight_g" in name:
            a__						='''weight_g'''
          elif "weight_v" in name:
            a__						='''weight_v'''
          elif "weight" in name:
            a__						='''weight'''
          elif "bias" in name:
            a__						='''bias'''
          else:
            a__						=None
          set_recursively(_A			,   _A			,   _A			,   _A			,   _A							)
        continue
    if not is_used:
      unused_weights.append(_A							)

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




def     UpperCAmelCase__       (    _A   :				Optional[int]			,   _A   :				str			,   _A   :				Optional[Any]			,   _A   :				Dict			,   _A   :				List[Any]							):





  '''simple docstring'''







  a__						=full_name.split('''conv_layers.'''							)[-1]
  a__						=name.split('''.'''							)
  a__						=int(items[0]							)
  a__						=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."""
      )
      a__						=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."""
      )
      a__						=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."
      )
      a__						=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."""
      )
      a__						=value
      logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""							)
  else:
    unused_weights.append(_A							)




def     UpperCAmelCase__       (    _A   :				str							):





  '''simple docstring'''







  a__						=HubertConfig()
  a__						=model.config

  a__						=fs_config.activation_dropout
  a__						=False
  a__						=fs_config.attention_dropout
  a__						=False
  a__						=eval(fs_config.extractor_conv_feature_layers							)
  a__						=[x[0] for x in conv_layers]
  a__						=[x[1] for x in conv_layers]
  a__						=[x[2] for x in conv_layers]
  a__						='''gelu'''
  a__						='''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
  a__						=False
  a__						=0.0
  a__						=0.0
  a__						=fs_config.activation_fn
  a__						=fs_config.dropout
  a__						=fs_config.encoder_embed_dim
  a__						=0.02
  a__						=fs_config.encoder_ffn_embed_dim
  a__						=1E-5
  a__						=0.0
  a__						=fs_config.encoder_attention_heads
  a__						=fs_config.conv_pos_groups
  a__						=fs_config.conv_pos
  a__						=len(_A							)
  a__						=fs_config.encoder_layers

  return config




@torch.no_grad()
def     UpperCAmelCase__       (    _A   :				Tuple			,   _A   :				List[Any]=None							):





  '''simple docstring'''







  a__						=distilhubert().model.model

  if config_path is not None:
    a__						=HubertConfig.from_pretrained(_A							)
  else:
    a__						=convert_config(_A							)
  a__						=model.eval()

  a__						=WavaVecaFeatureExtractor(
      feature_size=1			,   sampling_rate=1_60_00			,   padding_value=0			,   do_normalize=_A			,   return_attention_mask=_A			,   )
  a__						=HubertModel(_A							)

  recursively_load_weights(_A			,   _A							)

  feature_extractor.save_pretrained(_A							)
  hf_model.save_pretrained(_A							)


if __name__ == "__main__":
       lowerCamelCase     =      argparse.ArgumentParser()
       parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
       parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
       lowerCamelCase     =      parser.parse_args()
       convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase     =      {
    '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
    '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase     =      ['''DebertaV2TokenizerFast''']

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase     =      [
           '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFDebertaV2ForMaskedLM''',
           '''TFDebertaV2ForQuestionAnswering''',
           '''TFDebertaV2ForSequenceClassification''',
           '''TFDebertaV2ForTokenClassification''',
           '''TFDebertaV2Model''',
           '''TFDebertaV2PreTrainedModel''',
       ]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase     =      [
           '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''DebertaV2ForMaskedLM''',
           '''DebertaV2ForMultipleChoice''',
           '''DebertaV2ForQuestionAnswering''',
           '''DebertaV2ForSequenceClassification''',
           '''DebertaV2ForTokenClassification''',
           '''DebertaV2Model''',
           '''DebertaV2PreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_deberta_va import (
           DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
           DebertaVaConfig,
           DebertaVaOnnxConfig,
       )
       from .tokenization_deberta_va import DebertaVaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_deberta_va import (
                  TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFDebertaVaForMaskedLM,
                  TFDebertaVaForQuestionAnswering,
                  TFDebertaVaForSequenceClassification,
                  TFDebertaVaForTokenClassification,
                  TFDebertaVaModel,
                  TFDebertaVaPreTrainedModel,
              )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deberta_va import (
                  DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DebertaVaForMaskedLM,
                  DebertaVaForMultipleChoice,
                  DebertaVaForQuestionAnswering,
                  DebertaVaForSequenceClassification,
                  DebertaVaForTokenClassification,
                  DebertaVaModel,
                  DebertaVaPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase     =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
