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

_a      :			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',
}
_a      :			Union[str, Any]					=							[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def 				a__   (   a   :				Tuple       ,     a   :				List[Any]       ,     a   :				List[str]       ,     a   :				Tuple       ,     a   :				int		):

  """simple docstring"""







  for attribute in key.split("."		):
    _snake_case     :						Optional[int]						=   getattr(lowerCamelCase_       ,     lowerCamelCase_		)

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

  logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'		)
def 				a__   (   a   :				List[str]       ,     a   :				Dict       ,     a   :				Tuple		):

  """simple docstring"""







  _snake_case     :						Tuple						=   []
  _snake_case     :						Optional[int]						=   fairseq_model.state_dict()

  _snake_case     :						Tuple						=   hf_model.wavaveca_conformer.feature_extractor

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

  """simple docstring"""







  _snake_case     :						Optional[int]						=   full_name.split("conv_layers."		)[-1]
  _snake_case     :						Optional[int]						=   name.split("."		)
  _snake_case     :						Optional[int]						=   int(items[0]		)
  _snake_case     :						Optional[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.'		)
      _snake_case     :						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.'		)
      _snake_case     :						Optional[Any]						=   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.'		)
      _snake_case     :						Dict						=   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.'		)
      _snake_case     :						Tuple						=   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 				a__   (   a   :				List[Any]       ,     a   :				Dict       ,     a   :				Tuple=None       ,     a   :				Tuple=None       ,     a   :				List[str]=True		):

  """simple docstring"""







  if config_path is not None:
    _snake_case     :						Tuple						=   WavaVecaConformerConfig.from_pretrained(lowerCamelCase_       ,     hidden_act="swish"		)
  else:
    _snake_case     :						Optional[int]						=   WavaVecaConformerConfig()

  if "rope" in checkpoint_path:
    _snake_case     :						Tuple						=   '''rotary'''

  if is_finetuned:
    if dict_path:
      _snake_case     :						Union[str, Any]						=   Dictionary.load(lowerCamelCase_		)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      _snake_case     :						Dict						=   target_dict.pad_index
      _snake_case     :						int						=   target_dict.bos_index
      _snake_case     :						Tuple						=   target_dict.eos_index
      _snake_case     :						Optional[int]						=   len(target_dict.symbols		)
      _snake_case     :						Union[str, Any]						=   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_		)
      _snake_case     :						Dict						=   target_dict.indices

      # fairseq has the <pad> and <s> switched
      _snake_case     :						str						=   0
      _snake_case     :						Tuple						=   1
      with open(lowerCamelCase_       ,     "w"       ,     encoding="utf-8"		) as vocab_handle:
        json.dump(lowerCamelCase_       ,     lowerCamelCase_		)
      _snake_case     :						List[Any]						=   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     :						Optional[int]						=   True if config.feat_extract_norm == '''layer''' else False
      _snake_case     :						Union[str, 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     :						Union[str, Any]						=   WavaVecaConformerForCTC(lowerCamelCase_		)
  else:
    _snake_case     :						str						=   WavaVecaConformerForPreTraining(lowerCamelCase_		)

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

    _snake_case     :						List[Any]						=   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,     task=lowerCamelCase_		)

  _snake_case     :						List[str]						=   model[0].eval()

  recursively_load_weights(lowerCamelCase_       ,     lowerCamelCase_       ,     not is_finetuned		)

  hf_wavavec.save_pretrained(lowerCamelCase_		)


if __name__ == "__main__":
    _a      :			List[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      :			int					=							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"""
from __future__ import annotations

from collections import namedtuple
def 				a__   (   a   :				float       ,     a   :				float       ,     a   :				float		):

  """simple docstring"""







  _snake_case     :						Optional[Any]						=   namedtuple("result"       ,     "name value"		)
  if (voltage, current, power).count(0		) != 1:
    raise ValueError("Only one argument must be 0"		)
  elif power < 0:
    raise ValueError(
        "Power cannot be negative in any electrical/electronics system"		)
  elif voltage == 0:
    return result("voltage"       ,     power / current		)
  elif current == 0:
    return result("current"       ,     power / voltage		)
  elif power == 0:
    return result("power"       ,     float(round(abs(voltage * current		)       ,     2		)		)		)
  else:
    raise ValueError("Exactly one argument must be 0"		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

