import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__lowerCamelCase			: List[str]							   =		logging.get_logger(__name__)

__lowerCamelCase			: List[Any]							   =		{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
    '''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
    '''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
    '''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
    '''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
    '''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
    '''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
    '''self_attn.rotary_emb''': '''encoder.embed_positions''',
    '''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
    '''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
    '''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
    '''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
    '''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
    '''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
    '''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
    '''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
    '''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
    '''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
    '''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
    '''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
    '''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''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
__lowerCamelCase			: Tuple							   =		[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[Any] ,     __UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       List[str] ,     __UpperCamelCase    :       Optional[Any] ,     __UpperCamelCase    :       Tuple	)							->					Dict:







  """simple docstring"""





  for attribute in key.split("""."""	):
    SCREAMING_SNAKE_CASE__										=      getattr(__UpperCamelCase ,     __UpperCamelCase	)

  if weight_type is not None:
    SCREAMING_SNAKE_CASE__										=      getattr(__UpperCamelCase ,     __UpperCamelCase	).shape
  else:
    SCREAMING_SNAKE_CASE__										=      hf_pointer.shape

  if hf_shape != value.shape:
    raise ValueError(
        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":
    SCREAMING_SNAKE_CASE__										=      value
  elif weight_type == "weight_g":
    SCREAMING_SNAKE_CASE__										=      value
  elif weight_type == "weight_v":
    SCREAMING_SNAKE_CASE__										=      value
  elif weight_type == "bias":
    SCREAMING_SNAKE_CASE__										=      value
  elif weight_type == "running_mean":
    SCREAMING_SNAKE_CASE__										=      value
  elif weight_type == "running_var":
    SCREAMING_SNAKE_CASE__										=      value
  elif weight_type == "num_batches_tracked":
    SCREAMING_SNAKE_CASE__										=      value
  elif weight_type == "inv_freq":
    SCREAMING_SNAKE_CASE__										=      value
  else:
    SCREAMING_SNAKE_CASE__										=      value

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



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Tuple	)							->					Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      []
  SCREAMING_SNAKE_CASE__										=      fairseq_model.state_dict()

  SCREAMING_SNAKE_CASE__										=      hf_model.wavaveca_conformer.feature_extractor

  for name, value in fairseq_dict.items():
    SCREAMING_SNAKE_CASE__										=      False
    if "conv_layers" in name:
      load_conv_layer(
          __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     hf_model.config.feat_extract_norm == """group""" ,     )
      SCREAMING_SNAKE_CASE__										=      True
    else:
      for key, mapped_key in MAPPING.items():
        SCREAMING_SNAKE_CASE__										=      """wav2vec2_conformer.""" + 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]:
          SCREAMING_SNAKE_CASE__										=      True
          if "*" in mapped_key:
            SCREAMING_SNAKE_CASE__										=      name.split(__UpperCamelCase	)[0].split("""."""	)[-2]
            SCREAMING_SNAKE_CASE__										=      mapped_key.replace("""*""" ,     __UpperCamelCase	)
          if "pos_bias_u" in name:
            SCREAMING_SNAKE_CASE__										=      None
          elif "pos_bias_v" in name:
            SCREAMING_SNAKE_CASE__										=      None
          elif "weight_g" in name:
            SCREAMING_SNAKE_CASE__										=      """weight_g"""
          elif "weight_v" in name:
            SCREAMING_SNAKE_CASE__										=      """weight_v"""
          elif "bias" in name:
            SCREAMING_SNAKE_CASE__										=      """bias"""
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            SCREAMING_SNAKE_CASE__										=      """weight"""
          elif "running_mean" in name:
            SCREAMING_SNAKE_CASE__										=      """running_mean"""
          elif "inv_freq" in name:
            SCREAMING_SNAKE_CASE__										=      """inv_freq"""
          elif "running_var" in name:
            SCREAMING_SNAKE_CASE__										=      """running_var"""
          elif "num_batches_tracked" in name:
            SCREAMING_SNAKE_CASE__										=      """num_batches_tracked"""
          else:
            SCREAMING_SNAKE_CASE__										=      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 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Any ,     __UpperCamelCase    :       List[str] ,     __UpperCamelCase    :       Optional[Any] ,     __UpperCamelCase    :       int ,     __UpperCamelCase    :       Optional[Any]	)							->					str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      full_name.split("""conv_layers."""	)[-1]
  SCREAMING_SNAKE_CASE__										=      name.split("""."""	)
  SCREAMING_SNAKE_CASE__										=      int(items[0]	)
  SCREAMING_SNAKE_CASE__										=      int(items[1]	)

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



@torch.no_grad()
def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       int ,     __UpperCamelCase    :       List[Any]=None ,     __UpperCamelCase    :       Optional[Any]=None ,     __UpperCamelCase    :       Tuple=True	)							->					List[Any]:







  """simple docstring"""





  if config_path is not None:
    SCREAMING_SNAKE_CASE__										=      WavaVecaConformerConfig.from_pretrained(__UpperCamelCase ,     hidden_act="""swish"""	)
  else:
    SCREAMING_SNAKE_CASE__										=      WavaVecaConformerConfig()

  if "rope" in checkpoint_path:
    SCREAMING_SNAKE_CASE__										=      """rotary"""

  if is_finetuned:
    if dict_path:
      SCREAMING_SNAKE_CASE__										=      Dictionary.load(__UpperCamelCase	)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      SCREAMING_SNAKE_CASE__										=      target_dict.pad_index
      SCREAMING_SNAKE_CASE__										=      target_dict.bos_index
      SCREAMING_SNAKE_CASE__										=      target_dict.eos_index
      SCREAMING_SNAKE_CASE__										=      len(target_dict.symbols	)
      SCREAMING_SNAKE_CASE__										=      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	)
      SCREAMING_SNAKE_CASE__										=      target_dict.indices

      # fairseq has the <pad> and <s> switched
      SCREAMING_SNAKE_CASE__										=      0
      SCREAMING_SNAKE_CASE__										=      1
      with open(__UpperCamelCase ,     """w""" ,     encoding="""utf-8"""	) as vocab_handle:
        json.dump(__UpperCamelCase ,     __UpperCamelCase	)
      SCREAMING_SNAKE_CASE__										=      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 ,     )
      SCREAMING_SNAKE_CASE__										=      True if config.feat_extract_norm == """layer""" else False
      SCREAMING_SNAKE_CASE__										=      WavaVecaFeatureExtractor(
          feature_size=1 ,     sampling_rate=1_60_00 ,     padding_value=0 ,     do_normalize=__UpperCamelCase ,     return_attention_mask=__UpperCamelCase ,     )
      SCREAMING_SNAKE_CASE__										=      WavaVecaProcessor(feature_extractor=__UpperCamelCase ,     tokenizer=__UpperCamelCase	)
      processor.save_pretrained(__UpperCamelCase	)

    SCREAMING_SNAKE_CASE__										=      WavaVecaConformerForCTC(__UpperCamelCase	)
  else:
    SCREAMING_SNAKE_CASE__										=      WavaVecaConformerForPreTraining(__UpperCamelCase	)

  if is_finetuned:
    SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path] ,     arg_overrides={"""data""": """/""".join(dict_path.split("""/"""	)[:-1]	)}	)
  else:
    SCREAMING_SNAKE_CASE__										=      argparse.Namespace(task="""audio_pretraining"""	)
    SCREAMING_SNAKE_CASE__										=      fairseq.tasks.setup_task(__UpperCamelCase	)

    SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,     task=__UpperCamelCase	)

  SCREAMING_SNAKE_CASE__										=      model[0].eval()

  recursively_load_weights(__UpperCamelCase ,     __UpperCamelCase ,     not is_finetuned	)

  hf_wavavec.save_pretrained(__UpperCamelCase	)


if __name__ == "__main__":
   __lowerCamelCase			: 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'''
   )
   __lowerCamelCase			: Any							   =		parser.parse_args()
   convert_wavaveca_conformer_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
   )

import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def 	__SCREAMING_SNAKE_CASE  (					)							->					Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      ArgumentParser(
      description=(
          """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
      )	)

  # Optional arguments for the launch helper
  parser.add_argument("""--num_cores""" ,     type=__UpperCamelCase ,     default=1 ,     help="""Number of TPU cores to use (1 or 8)."""	)

  # positional
  parser.add_argument(
      """training_script""" ,     type=__UpperCamelCase ,     help=(
          """The full path to the single TPU training """
          """program/script to be launched in parallel, """
          """followed by all the arguments for the """
          """training script"""
      ) ,     )

  # rest from the training program
  parser.add_argument("""training_script_args""" ,     nargs=__UpperCamelCase	)

  return parser.parse_args()



def 	__SCREAMING_SNAKE_CASE  (					)							->					Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      parse_args()

  # Import training_script as a module.
  SCREAMING_SNAKE_CASE__										=      Path(args.training_script	)
  sys.path.append(str(script_fpath.parent.resolve()	)	)
  SCREAMING_SNAKE_CASE__										=      script_fpath.stem
  SCREAMING_SNAKE_CASE__										=      importlib.import_module(__UpperCamelCase	)

  # Patch sys.argv
  SCREAMING_SNAKE_CASE__										=      [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores	)]

  xmp.spawn(mod._mp_fn ,     args=() ,     nprocs=args.num_cores	)


if __name__ == "__main__":
   main()
