





'''simple docstring'''


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()
A_				     =							logging.get_logger(__name__)

A_				     =							{
    "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       A							(      _UpperCAmelCase   :				List[Any]						,_UpperCAmelCase   :				int						,_UpperCAmelCase   :				str						,_UpperCAmelCase   :				str						,_UpperCAmelCase   :				Optional[int]					)  ->				Optional[Any]:


  '''simple docstring'''




  for attribute in key.split('.'					):
    __lowerCAmelCase		:    Union[str, Any]			     =  getattr(_UpperCAmelCase						,_UpperCAmelCase					)

  if weight_type is not None:
    __lowerCAmelCase		:    Dict			     =  getattr(_UpperCAmelCase						,_UpperCAmelCase					).shape
  else:
    __lowerCAmelCase		:    List[str]			     =  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":
    __lowerCAmelCase		:    Optional[int]			     =  value
  elif weight_type == "weight_g":
    __lowerCAmelCase		:    Tuple			     =  value
  elif weight_type == "weight_v":
    __lowerCAmelCase		:    Optional[Any]			     =  value
  elif weight_type == "bias":
    __lowerCAmelCase		:    Optional[int]			     =  value
  else:
    __lowerCAmelCase		:    Optional[int]			     =  value

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




def       A							(      _UpperCAmelCase   :				Union[str, Any]						,_UpperCAmelCase   :				Optional[int]						,_UpperCAmelCase   :				int					)  ->				List[str]:


  '''simple docstring'''




  __lowerCAmelCase		:    Dict			     =  []
  __lowerCAmelCase		:    Tuple			     =  fairseq_model.state_dict()

  __lowerCAmelCase		:    List[str]			     =  hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    __lowerCAmelCase		:    List[str]			     =  False
    if "conv_layers" in name:
      load_conv_layer(
          _UpperCAmelCase						,_UpperCAmelCase						,_UpperCAmelCase						,_UpperCAmelCase						,hf_model.config.feat_extract_norm == 'group'						,)
      __lowerCAmelCase		:    Tuple			     =  True
    else:
      for key, mapped_key in MAPPING.items():
        __lowerCAmelCase		:    Optional[Any]			     =  '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]:
          __lowerCAmelCase		:    Tuple			     =  True
          if "*" in mapped_key:
            __lowerCAmelCase		:    Optional[int]			     =  name.split(_UpperCAmelCase					)[0].split('.'					)[-2]
            if not layer_index.isnumeric():
              continue
            __lowerCAmelCase		:    Optional[Any]			     =  mapped_key.replace('*'						,_UpperCAmelCase					)
          if "weight_g" in name:
            __lowerCAmelCase		:    Dict			     =  'weight_g'
          elif "weight_v" in name:
            __lowerCAmelCase		:    Dict			     =  'weight_v'
          elif "weight" in name:
            __lowerCAmelCase		:    Union[str, Any]			     =  'weight'
          elif "bias" in name:
            __lowerCAmelCase		:    int			     =  'bias'
          else:
            __lowerCAmelCase		:    Any			     =  None
          set_recursively(_UpperCAmelCase						,_UpperCAmelCase						,_UpperCAmelCase						,_UpperCAmelCase						,_UpperCAmelCase					)
        continue
    if not is_used:
      unused_weights.append(_UpperCAmelCase					)

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




def       A							(      _UpperCAmelCase   :				List[Any]						,_UpperCAmelCase   :				Union[str, Any]						,_UpperCAmelCase   :				List[Any]						,_UpperCAmelCase   :				Dict						,_UpperCAmelCase   :				Tuple					)  ->				Any:


  '''simple docstring'''




  __lowerCAmelCase		:    List[Any]			     =  full_name.split('conv_layers.'					)[-1]
  __lowerCAmelCase		:    str			     =  name.split('.'					)
  __lowerCAmelCase		:    Tuple			     =  int(items[0]					)
  __lowerCAmelCase		:    List[str]			     =  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."""
      )
      __lowerCAmelCase		:    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."""
      )
      __lowerCAmelCase		:    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."
      )
      __lowerCAmelCase		:    Any			     =  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."""
      )
      __lowerCAmelCase		:    Union[str, Any]			     =  value
      logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
  else:
    unused_weights.append(_UpperCAmelCase					)




def       A							(      _UpperCAmelCase   :				List[str]						,_UpperCAmelCase   :				str					)  ->				Any:


  '''simple docstring'''




  __lowerCAmelCase		:    Union[str, Any]			     =  SEWDConfig()
  if is_finetuned:
    __lowerCAmelCase		:    Dict			     =  model.wav_encoder.wav_model.cfg
  else:
    __lowerCAmelCase		:    Optional[int]			     =  model.cfg

  __lowerCAmelCase		:    int			     =  fs_config.conv_bias
  __lowerCAmelCase		:    Any			     =  eval(fs_config.conv_feature_layers					)
  __lowerCAmelCase		:    Tuple			     =  [x[0] for x in conv_layers]
  __lowerCAmelCase		:    Optional[int]			     =  [x[1] for x in conv_layers]
  __lowerCAmelCase		:    Dict			     =  [x[2] for x in conv_layers]
  __lowerCAmelCase		:    int			     =  'gelu'
  __lowerCAmelCase		:    List[str]			     =  'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
  __lowerCAmelCase		:    Any			     =  0.0
  __lowerCAmelCase		:    Any			     =  fs_config.activation_fn.name
  __lowerCAmelCase		:    Optional[int]			     =  fs_config.encoder_embed_dim
  __lowerCAmelCase		:    Optional[Any]			     =  0.02
  __lowerCAmelCase		:    Union[str, Any]			     =  fs_config.encoder_ffn_embed_dim
  __lowerCAmelCase		:    List[str]			     =  1e-5
  __lowerCAmelCase		:    List[Any]			     =  fs_config.encoder_layerdrop
  __lowerCAmelCase		:    Union[str, Any]			     =  fs_config.encoder_attention_heads
  __lowerCAmelCase		:    Optional[int]			     =  fs_config.conv_pos_groups
  __lowerCAmelCase		:    str			     =  fs_config.conv_pos
  __lowerCAmelCase		:    int			     =  len(_UpperCAmelCase					)
  __lowerCAmelCase		:    Optional[int]			     =  fs_config.encoder_layers
  __lowerCAmelCase		:    Union[str, Any]			     =  fs_config.squeeze_factor
  # DeBERTa-specific parameters:
  __lowerCAmelCase		:    Union[str, Any]			     =  fs_config.max_position_embeddings
  __lowerCAmelCase		:    Dict			     =  fs_config.position_buckets
  __lowerCAmelCase		:    str			     =  fs_config.share_att_key
  __lowerCAmelCase		:    Tuple			     =  fs_config.relative_attention
  __lowerCAmelCase		:    Optional[int]			     =  fs_config.position_biased_input
  __lowerCAmelCase		:    List[str]			     =  tuple(fs_config.pos_att_type.split('|'					)					)
  __lowerCAmelCase		:    List[Any]			     =  fs_config.norm_rel_ebd

  # take care of any params that are overridden by the Wav2VecCtc model
  if is_finetuned:
    __lowerCAmelCase		:    Dict			     =  model.cfg
    __lowerCAmelCase		:    Dict			     =  fs_config.final_dropout
    __lowerCAmelCase		:    Dict			     =  fs_config.layerdrop
  __lowerCAmelCase		:    List[Any]			     =  fs_config.activation_dropout
  __lowerCAmelCase		:    Tuple			     =  fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
  __lowerCAmelCase		:    List[str]			     =  fs_config.attention_dropout
  __lowerCAmelCase		:    List[str]			     =  fs_config.dropout_input
  __lowerCAmelCase		:    Union[str, Any]			     =  fs_config.dropout
  __lowerCAmelCase		:    Optional[Any]			     =  fs_config.mask_channel_length
  __lowerCAmelCase		:    int			     =  fs_config.mask_channel_prob
  __lowerCAmelCase		:    Any			     =  fs_config.mask_length
  __lowerCAmelCase		:    str			     =  fs_config.mask_prob

  __lowerCAmelCase		:    Any			     =  'Wav2Vec2FeatureExtractor'
  __lowerCAmelCase		:    Tuple			     =  'Wav2Vec2CTCTokenizer'

  return config




@torch.no_grad()
def       A							(      _UpperCAmelCase   :				Optional[Any]						,_UpperCAmelCase   :				Any						,_UpperCAmelCase   :				Union[str, Any]=None						,_UpperCAmelCase   :				List[str]=None						,_UpperCAmelCase   :				Dict=True					)  ->				Optional[int]:


  '''simple docstring'''




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

  if config_path is not None:
    __lowerCAmelCase		:    Dict			     =  SEWDConfig.from_pretrained(_UpperCAmelCase					)
  else:
    __lowerCAmelCase		:    Any			     =  convert_config(model[0]						,_UpperCAmelCase					)
  __lowerCAmelCase		:    List[Any]			     =  model[0].eval()

  __lowerCAmelCase		:    Optional[Any]			     =  True if config.feat_extract_norm == 'layer' else False
  __lowerCAmelCase		:    Union[str, Any]			     =  WavaVecaFeatureExtractor(
      feature_size=1						,sampling_rate=1_6_0_0_0						,padding_value=0						,do_normalize=_UpperCAmelCase						,return_attention_mask=_UpperCAmelCase						,)

  if is_finetuned:
    if dict_path:
      __lowerCAmelCase		:    Tuple			     =  Dictionary.load(_UpperCAmelCase					)

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

    __lowerCAmelCase		:    Dict			     =  SEWDForCTC(_UpperCAmelCase					)
  else:
    __lowerCAmelCase		:    Any			     =  SEWDModel(_UpperCAmelCase					)
    feature_extractor.save_pretrained(_UpperCAmelCase					)

  recursively_load_weights(_UpperCAmelCase						,_UpperCAmelCase						,_UpperCAmelCase					)

  hf_model.save_pretrained(_UpperCAmelCase					)


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(
        "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
    )
    A_				     =							parser.parse_args()
    convert_sew_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
    )












'''simple docstring'''


from __future__ import annotations




def       A							(      _UpperCAmelCase   :				list					)  ->				int | float:


  '''simple docstring'''




  __lowerCAmelCase		:    List[Any]			     =  sorted(_UpperCAmelCase					)
  __lowerCAmelCase		:    List[str]			     =  len(_UpperCAmelCase					)
  __lowerCAmelCase		:    Any			     =  length >> 1
  return (
      (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
      if length % 2 == 0
      else sorted_list[mid_index]
  )




def       A							(      )  ->				Union[str, Any]:


  '''simple docstring'''




  import doctest

  doctest.testmod()


if __name__ == "__main__":
    main()





