


'''simple docstring'''






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__A :  int    							=				logging.get_logger(__name__)

__A :  str    							=				{
    "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",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
__A :  Optional[Any]    							=				[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def     UpperCamelCase_      (   A__ :       List[str]							,   A__ :       str							,   A__ :       int							,   A__ :       int							,   A__ :       str							,   A__ :       List[str] ):
  '''simple docstring'''





  for attribute in key.split(""".""" ):
    if is_finetuned:
      if attribute in ["quantizer", "project_q", "project_hid"]:
        # those layers are only relevant for pretraining and should be dropped
        return

      if attribute == "ctc_proj":
        # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
        lowerCAmelCase_      :				Tuple      =       """lm_head"""

    lowerCAmelCase_      :				str      =       getattr(A__							,   A__ )

  if weight_type is not None:
    lowerCAmelCase_      :				Optional[Any]      =       getattr(A__							,   A__ ).shape
  else:
    lowerCAmelCase_      :				List[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":
    lowerCAmelCase_      :				Optional[int]      =       value
  elif weight_type == "weight_g":
    lowerCAmelCase_      :				int      =       value
  elif weight_type == "weight_v":
    lowerCAmelCase_      :				Optional[Any]      =       value
  elif weight_type == "bias":
    lowerCAmelCase_      :				List[Any]      =       value
  else:
    lowerCAmelCase_      :				Any      =       value

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







def     UpperCamelCase_      (   A__ :       Optional[int]							,   A__ :       List[Any]							,   A__ :       int ):
  '''simple docstring'''





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

  lowerCAmelCase_      :				Dict      =       hf_model.unispeech.feature_extractor

  for name, value in fairseq_dict.items():
    lowerCAmelCase_      :				str      =       False
    if "conv_layers" in name:
      load_conv_layer(
          A__							,   A__							,   A__							,   A__							,   hf_model.config.feat_extract_norm == """group"""							,   )
      lowerCAmelCase_      :				List[str]      =       True
    else:
      for key, mapped_key in MAPPING.items():
        lowerCAmelCase_      :				Union[str, Any]      =       """unispeech.""" + 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]:
          lowerCAmelCase_      :				Union[str, Any]      =       True
          if "*" in mapped_key:
            lowerCAmelCase_      :				Any      =       name.split(A__ )[0].split(""".""" )[-2]
            lowerCAmelCase_      :				Optional[int]      =       mapped_key.replace("""*"""							,   A__ )
          if "weight_g" in name:
            lowerCAmelCase_      :				int      =       """weight_g"""
          elif "weight_v" in name:
            lowerCAmelCase_      :				List[str]      =       """weight_v"""
          elif "bias" in name:
            lowerCAmelCase_      :				List[str]      =       """bias"""
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            lowerCAmelCase_      :				List[str]      =       """weight"""
          else:
            lowerCAmelCase_      :				List[Any]      =       None
          set_recursively(A__							,   A__							,   A__							,   A__							,   A__							,   A__ )
        continue
    if not is_used:
      unused_weights.append(A__ )

  logger.warning(f'Unused weights: {unused_weights}' )







def     UpperCamelCase_      (   A__ :       str							,   A__ :       Union[str, Any]							,   A__ :       int							,   A__ :       Optional[int]							,   A__ :       Optional[int] ):
  '''simple docstring'''





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







@torch.no_grad()
def     UpperCamelCase_      (   A__ :       int							,   A__ :       List[Any]							,   A__ :       str=None							,   A__ :       List[str]=None							,   A__ :       str=True ):
  '''simple docstring'''





  if config_path is not None:
    lowerCAmelCase_      :				Optional[Any]      =       UniSpeechConfig.from_pretrained(A__ )
  else:
    lowerCAmelCase_      :				Dict      =       UniSpeechConfig()

  if is_finetuned:
    if dict_path:
      lowerCAmelCase_      :				Dict      =       Dictionary.load_from_json(A__ )

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

      # fairseq has the <pad> and <s> switched
      lowerCAmelCase_      :				Tuple      =       42
      lowerCAmelCase_      :				int      =       43
      with open(A__							,   """w"""							,   encoding="""utf-8""" ) as vocab_handle:
        json.dump(A__							,   A__ )
      lowerCAmelCase_      :				Any      =       WavaVecaPhonemeCTCTokenizer(
          A__							,   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=A__							,   )
      lowerCAmelCase_      :				Optional[int]      =       True if config.feat_extract_norm == """layer""" else False
      lowerCAmelCase_      :				Optional[int]      =       WavaVecaFeatureExtractor(
          feature_size=1							,   sampling_rate=1_60_00							,   padding_value=0							,   do_normalize=A__							,   return_attention_mask=A__							,   )
      lowerCAmelCase_      :				List[str]      =       WavaVecaProcessor(feature_extractor=A__							,   tokenizer=A__ )
      processor.save_pretrained(A__ )

    lowerCAmelCase_      :				Union[str, Any]      =       UniSpeechForCTC(A__ )
  else:
    lowerCAmelCase_      :				List[str]      =       UniSpeechForPreTraining(A__ )

  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] ), """w2v_path""": checkpoint_path} )
  else:
    lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_      :				Tuple      =       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )

  lowerCAmelCase_      :				Union[str, Any]      =       model[0].eval()

  recursively_load_weights(A__							,   A__							,   A__ )

  hf_unispeech.save_pretrained(A__ )


if __name__ == "__main__":
  __A :  List[str]    							=				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 :  Any    							=				parser.parse_args()
  convert_unispeech_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A :  List[Any]    							=				{
    "configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
    "tokenization_roc_bert": ["RoCBertTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  pass

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A :  Union[str, Any]    							=				[
      "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RoCBertForCausalLM",
      "RoCBertForMaskedLM",
      "RoCBertForMultipleChoice",
      "RoCBertForPreTraining",
      "RoCBertForQuestionAnswering",
      "RoCBertForSequenceClassification",
      "RoCBertForTokenClassification",
      "RoCBertLayer",
      "RoCBertModel",
      "RoCBertPreTrainedModel",
      "load_tf_weights_in_roc_bert",
  ]

if TYPE_CHECKING:
  from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
  from .tokenization_roc_bert import RoCBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    raise OptionalDependencyNotAvailable()

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roc_bert import (
        ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        RoCBertForCausalLM,
        RoCBertForMaskedLM,
        RoCBertForMultipleChoice,
        RoCBertForPreTraining,
        RoCBertForQuestionAnswering,
        RoCBertForSequenceClassification,
        RoCBertForTokenClassification,
        RoCBertLayer,
        RoCBertModel,
        RoCBertPreTrainedModel,
        load_tf_weights_in_roc_bert,
    )


else:
  import sys

  __A :  int    							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



