



'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					Dict:
      for attribute in key.split("""."""						):
            snake_case__    :       Any       		= getattr(_lowerCAmelCase	,  _lowerCAmelCase						)

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

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





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

      snake_case__    :       Any       		= hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            snake_case__    :       Optional[Any]       		= False
            if "conv_layers" in name:
                  load_conv_layer(
                      _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  hf_model.config.feat_extract_norm == """group"""	,  )
                  snake_case__    :       Any       		= True
            else:
                  for key, mapped_key in MAPPING.items():
                        snake_case__    :       Optional[int]       		= """hubert.""" + 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] and not is_finetuned):
                              snake_case__    :       List[Any]       		= True
                              if "*" in mapped_key:
                                    snake_case__    :       Any       		= name.split(_lowerCAmelCase						)[0].split("""."""						)[-2]
                                    snake_case__    :       str       		= mapped_key.replace("""*"""	,  _lowerCAmelCase						)
                              if "weight_g" in name:
                                    snake_case__    :       Optional[int]       		= """weight_g"""
                              elif "weight_v" in name:
                                    snake_case__    :       str       		= """weight_v"""
                              elif "weight" in name:
                                    snake_case__    :       str       		= """weight"""
                              elif "bias" in name:
                                    snake_case__    :       List[Any]       		= """bias"""
                              else:
                                    snake_case__    :       List[str]       		= 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 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					Any:
      snake_case__    :       Optional[Any]       		= full_name.split("""conv_layers."""						)[-1]
      snake_case__    :       Union[str, Any]       		= name.split("""."""						)
      snake_case__    :       Dict       		= int(items[0]						)
      snake_case__    :       List[Any]       		= 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."
                  )
                  snake_case__    :       Any       		= 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."
                  )
                  snake_case__    :       Optional[int]       		= 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."
                  )
                  snake_case__    :       int       		= 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."
                  )
                  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(_lowerCAmelCase						)





@torch.no_grad()
def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase=None	,  _lowerCAmelCase=None	,  _lowerCAmelCase=True						)						->					Dict:
      if config_path is not None:
            snake_case__    :       List[Any]       		= HubertConfig.from_pretrained(_lowerCAmelCase						)
      else:
            snake_case__    :       Tuple       		= HubertConfig()

      if is_finetuned:
            if dict_path:
                  snake_case__    :       Optional[Any]       		= Dictionary.load(_lowerCAmelCase						)

                  # 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__    :       List[str]       		= target_dict.bos_index
                  snake_case__    :       List[str]       		= target_dict.eos_index
                  snake_case__    :       Optional[Any]       		= len(target_dict.symbols						)
                  snake_case__    :       int       		= 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						)
                  snake_case__    :       str       		= 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	,  )
                  snake_case__    :       List[str]       		= True if config.feat_extract_norm == """layer""" else False
                  snake_case__    :       Any       		= WavaVecaFeatureExtractor(
                      feature_size=1	,  sampling_rate=16_000	,  padding_value=0	,  do_normalize=_lowerCAmelCase	,  return_attention_mask=_lowerCAmelCase	,  )
                  snake_case__    :       str       		= WavaVecaProcessor(feature_extractor=_lowerCAmelCase	,  tokenizer=_lowerCAmelCase						)
                  processor.save_pretrained(_lowerCAmelCase						)

            snake_case__    :       Tuple       		= HubertForCTC(_lowerCAmelCase						)
      else:
            snake_case__    :       Tuple       		= HubertModel(_lowerCAmelCase						)

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

      snake_case__    :       int       		= model[0].eval()

      recursively_load_weights(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)

      hf_wavavec.save_pretrained(_lowerCAmelCase						)


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"
       )
       __a  			=						parser.parse_args()
       convert_hubert_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )





'''simple docstring'''





# flake8: noqa
# Lint as: python3
__a  			=						[
    "VerificationMode",
    "Version",
    "disable_progress_bar",
    "enable_progress_bar",
    "is_progress_bar_enabled",
    "experimental",
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental
