
'''simple docstring'''
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()
A__   :		Optional[Any] 		=logging.get_logger(__name__)

A__   :		Union[str, 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',
}
A__   :		int 		=[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]







def        A_     (						__SCREAMING_SNAKE_CASE						:  Union[str, Any]			,     __SCREAMING_SNAKE_CASE						:  List[str]			,     __SCREAMING_SNAKE_CASE						:  List[Any]			,     __SCREAMING_SNAKE_CASE						:  str			,     __SCREAMING_SNAKE_CASE						:  List[str] )				->       List[Any]:





  """simple docstring"""

  for attribute in key.split(""".""" ):
    __A		:     Tuple												=		getattr(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

  if weight_type is not None:
    __A		:     List[str]												=		getattr(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ).shape
  else:
    __A		:     Dict												=		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":
    __A		:     Optional[int]												=		value
  elif weight_type == "weight_g":
    __A		:     Any												=		value
  elif weight_type == "weight_v":
    __A		:     Any												=		value
  elif weight_type == "bias":
    __A		:     Tuple												=		value
  elif weight_type == "running_mean":
    __A		:     Any												=		value
  elif weight_type == "running_var":
    __A		:     int												=		value
  elif weight_type == "num_batches_tracked":
    __A		:     List[Any]												=		value
  elif weight_type == "inv_freq":
    __A		:     Optional[Any]												=		value
  else:
    __A		:     List[str]												=		value

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







def        A_     (						__SCREAMING_SNAKE_CASE						:  List[Any]			,     __SCREAMING_SNAKE_CASE						:  List[str]			,     __SCREAMING_SNAKE_CASE						:  Any )				->       Any:





  """simple docstring"""

  __A		:     Tuple												=		[]
  __A		:     int												=		fairseq_model.state_dict()

  __A		:     Union[str, Any]												=		hf_model.wavaveca_conformer.feature_extractor

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

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







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





  """simple docstring"""

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







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





  """simple docstring"""

  if config_path is not None:
    __A		:     Optional[Any]												=		WavaVecaConformerConfig.from_pretrained(__SCREAMING_SNAKE_CASE			,     hidden_act="""swish""" )
  else:
    __A		:     int												=		WavaVecaConformerConfig()

  if "rope" in checkpoint_path:
    __A		:     List[Any]												=		"""rotary"""

  if is_finetuned:
    if dict_path:
      __A		:     Any												=		Dictionary.load(__SCREAMING_SNAKE_CASE )

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

      # fairseq has the <pad> and <s> switched
      __A		:     Union[str, Any]												=		0
      __A		:     List[Any]												=		1
      with open(__SCREAMING_SNAKE_CASE			,     """w"""			,     encoding="""utf-8""" ) as vocab_handle:
        json.dump(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
      __A		:     List[str]												=		WavaVecaCTCTokenizer(
          __SCREAMING_SNAKE_CASE			,     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=__SCREAMING_SNAKE_CASE			,     )
      __A		:     List[str]												=		True if config.feat_extract_norm == """layer""" else False
      __A		:     str												=		WavaVecaFeatureExtractor(
          feature_size=1			,     sampling_rate=1_6000			,     padding_value=0			,     do_normalize=__SCREAMING_SNAKE_CASE			,     return_attention_mask=__SCREAMING_SNAKE_CASE			,     )
      __A		:     Dict												=		WavaVecaProcessor(feature_extractor=__SCREAMING_SNAKE_CASE			,     tokenizer=__SCREAMING_SNAKE_CASE )
      processor.save_pretrained(__SCREAMING_SNAKE_CASE )

    __A		:     str												=		WavaVecaConformerForCTC(__SCREAMING_SNAKE_CASE )
  else:
    __A		:     List[Any]												=		WavaVecaConformerForPreTraining(__SCREAMING_SNAKE_CASE )

  if is_finetuned:
    __A							,					__A							,					__A		:     Optional[int]												=		fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]			,     arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
  else:
    __A		:     Any												=		argparse.Namespace(task="""audio_pretraining""" )
    __A		:     List[str]												=		fairseq.tasks.setup_task(__SCREAMING_SNAKE_CASE )

    __A							,					__A							,					__A		:     str												=		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			,     task=__SCREAMING_SNAKE_CASE )

  __A		:     List[Any]												=		model[0].eval()

  recursively_load_weights(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     not is_finetuned )

  hf_wavavec.save_pretrained(__SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
     A__   :		Any 		=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__   :		str 		=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
     )





'''simple docstring'''
A__   :		int 		=8.3_1_4_4_6_2  # Unit - J mol-1 K-1







def        A_     (						__SCREAMING_SNAKE_CASE						:  float			,     __SCREAMING_SNAKE_CASE						:  float			,     __SCREAMING_SNAKE_CASE						:  float )				->       float:





  """simple docstring"""

  if moles < 0 or kelvin < 0 or volume < 0:
    raise ValueError("""Invalid inputs. Enter positive value.""" )
  return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume







def        A_     (						__SCREAMING_SNAKE_CASE						:  float			,     __SCREAMING_SNAKE_CASE						:  float			,     __SCREAMING_SNAKE_CASE						:  float )				->       float:





  """simple docstring"""

  if moles < 0 or kelvin < 0 or pressure < 0:
    raise ValueError("""Invalid inputs. Enter positive value.""" )
  return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
     from doctest import testmod

     testmod()



