
"""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()
UpperCAmelCase__          = logging.get_logger(__name__)

UpperCAmelCase__          = {
    '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',
}
UpperCAmelCase__          = [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       List[str]		)     ->					Optional[Any]:
  for attribute in key.split('''.'''		):
    _snake_case					     =       getattr(__lowerCamelCase ,					__lowerCamelCase		)

  if weight_type is not None:
    _snake_case					     =       getattr(__lowerCamelCase ,					__lowerCamelCase		).shape
  else:
    _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":
    _snake_case					     =       value
  elif weight_type == "weight_g":
    _snake_case					     =       value
  elif weight_type == "weight_v":
    _snake_case					     =       value
  elif weight_type == "bias":
    _snake_case					     =       value
  elif weight_type == "running_mean":
    _snake_case					     =       value
  elif weight_type == "running_var":
    _snake_case					     =       value
  elif weight_type == "num_batches_tracked":
    _snake_case					     =       value
  elif weight_type == "inv_freq":
    _snake_case					     =       value
  else:
    _snake_case					     =       value

  logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[int] ,					__lowerCamelCase   :       Any ,					__lowerCamelCase   :       str		)     ->					Union[str, Any]:
  _snake_case					     =       []
  _snake_case					     =       fairseq_model.state_dict()

  _snake_case					     =       hf_model.wavaveca_conformer.feature_extractor

  for name, value in fairseq_dict.items():
    _snake_case					     =       False
    if "conv_layers" in name:
      load_conv_layer(
          __lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase ,					hf_model.config.feat_extract_norm == '''group''' ,					)
      _snake_case					     =       True
    else:
      for key, mapped_key in MAPPING.items():
        _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]:
          _snake_case					     =       True
          if "*" in mapped_key:
            _snake_case					     =       name.split(__lowerCamelCase		)[0].split('''.'''		)[-2]
            _snake_case					     =       mapped_key.replace('''*''' ,					__lowerCamelCase		)
          if "pos_bias_u" in name:
            _snake_case					     =       None
          elif "pos_bias_v" in name:
            _snake_case					     =       None
          elif "weight_g" in name:
            _snake_case					     =       '''weight_g'''
          elif "weight_v" in name:
            _snake_case					     =       '''weight_v'''
          elif "bias" in name:
            _snake_case					     =       '''bias'''
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            _snake_case					     =       '''weight'''
          elif "running_mean" in name:
            _snake_case					     =       '''running_mean'''
          elif "inv_freq" in name:
            _snake_case					     =       '''inv_freq'''
          elif "running_var" in name:
            _snake_case					     =       '''running_var'''
          elif "num_batches_tracked" in name:
            _snake_case					     =       '''num_batches_tracked'''
          else:
            _snake_case					     =       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 					_UpperCAmelCase      ( __lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       Dict		)     ->					int:
  _snake_case					     =       full_name.split('''conv_layers.'''		)[-1]
  _snake_case					     =       name.split('''.'''		)
  _snake_case					     =       int(items[0]		)
  _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.'''		)
      _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.'''		)
      _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.'''		)
      _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.'''		)
      _snake_case					     =       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 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Any ,					__lowerCamelCase   :       List[Any]=None ,					__lowerCamelCase   :       List[str]=None ,					__lowerCamelCase   :       Union[str, Any]=True		)     ->					Any:
  if config_path is not None:
    _snake_case					     =       WavaVecaConformerConfig.from_pretrained(__lowerCamelCase ,					hidden_act='''swish'''		)
  else:
    _snake_case					     =       WavaVecaConformerConfig()

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

  if is_finetuned:
    if dict_path:
      _snake_case					     =       Dictionary.load(__lowerCamelCase		)

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

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

    _snake_case					     =       WavaVecaConformerForCTC(__lowerCamelCase		)
  else:
    _snake_case					     =       WavaVecaConformerForPreTraining(__lowerCamelCase		)

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

    _snake_case     ,					_snake_case     ,					_snake_case					     =       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,					task=__lowerCamelCase		)

  _snake_case					     =       model[0].eval()

  recursively_load_weights(__lowerCamelCase ,					__lowerCamelCase ,					not is_finetuned		)

  hf_wavavec.save_pretrained(__lowerCamelCase		)


if __name__ == "__main__":
 UpperCAmelCase__          = 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'
 )
 UpperCAmelCase__          = 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"""



def 					_UpperCAmelCase      ( __lowerCamelCase   :       list ,					__lowerCamelCase   :       int = 0		)     ->					list:
  _snake_case					     =       length or len(__lowerCamelCase		)
  _snake_case					     =       False
  for i in range(length - 1		):
    if list_data[i] > list_data[i + 1]:
      _snake_case     ,					_snake_case					     =       list_data[i + 1], list_data[i]
      _snake_case					     =       True

  return list_data if not swapped else bubble_sort(__lowerCamelCase ,					length - 1		)


if __name__ == "__main__":
 import doctest

 doctest.testmod()



