



"""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													=   logging.get_logger(__name__)

_A													=   {
    "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													=   [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]



def 			lowercase	(_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case			)       ->    Tuple:




  '''simple docstring'''







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

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

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



def 			lowercase	(_snake_case ,_snake_case ,_snake_case			)       ->    Optional[Any]:




  '''simple docstring'''







  __UpperCamelCase  						=							[]
  __UpperCamelCase  						=							fairseq_model.state_dict()

  __UpperCamelCase  						=							hf_model.wavaveca_conformer.feature_extractor

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

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



def 			lowercase	(_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case			)       ->    Any:




  '''simple docstring'''







  __UpperCamelCase  						=							full_name.split("conv_layers."			)[-1]
  __UpperCamelCase  						=							name.split("."			)
  __UpperCamelCase  						=							int(items[0]			)
  __UpperCamelCase  						=							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."""			)
      __UpperCamelCase  						=							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."""			)
      __UpperCamelCase  						=							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."""			)
      __UpperCamelCase  						=							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."""			)
      __UpperCamelCase  						=							value
      logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""			)
  else:
    unused_weights.append(_snake_case			)



@torch.no_grad()
def 			lowercase	(_snake_case ,_snake_case ,_snake_case=None ,_snake_case=None ,_snake_case=True			)       ->    List[Any]:




  '''simple docstring'''







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

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

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

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

      # fairseq has the <pad> and <s> switched
      __UpperCamelCase  						=							0
      __UpperCamelCase  						=							1
      with open(_snake_case ,"w" ,encoding="utf-8"			) as vocab_handle:
        json.dump(_snake_case ,_snake_case			)
      __UpperCamelCase  						=							WavaVecaCTCTokenizer(
          _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=_snake_case ,)
      __UpperCamelCase  						=							True if config.feat_extract_norm == "layer" else False
      __UpperCamelCase  						=							WavaVecaFeatureExtractor(
          feature_size=1 ,sampling_rate=16000 ,padding_value=0 ,do_normalize=_snake_case ,return_attention_mask=_snake_case ,)
      __UpperCamelCase  						=							WavaVecaProcessor(feature_extractor=_snake_case ,tokenizer=_snake_case			)
      processor.save_pretrained(_snake_case			)

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

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

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

  __UpperCamelCase  						=							model[0].eval()

  recursively_load_weights(_snake_case ,_snake_case ,not is_finetuned			)

  hf_wavavec.save_pretrained(_snake_case			)


if __name__ == "__main__":
 _A													=   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													=   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




def 							lowerCamelCase	(					_snake_case	:    list[int]							):





    '''simple docstring'''


    return len(set(_snake_case							)							) == len(_snake_case							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()




