



"""simple docstring"""
import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    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.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						int:







  '''simple docstring'''


  for attribute in key.split("""."""							):
    lowercase_								= getattr(__snake_case ,  __snake_case							)

  if weight_type is not None:
    lowercase_								= getattr(__snake_case ,  __snake_case							).shape
  else:
    lowercase_								= hf_pointer.shape

  assert hf_shape == value.shape, (
      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":
    lowercase_								= value
  elif weight_type == "weight_g":
    lowercase_								= value
  elif weight_type == "weight_v":
    lowercase_								= value
  elif weight_type == "bias":
    lowercase_								= value
  else:
    lowercase_								= value

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


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						List[str]:







  '''simple docstring'''


  lowercase_								= []
  lowercase_								= fairseq_model.state_dict()

  lowercase_								= hf_model.feature_extractor

  # if encoder has different dim to decoder -> use proj_weight
  lowercase_								= None

  for name, value in fairseq_dict.items():
    lowercase_								= False
    if "conv_layers" in name:
      load_conv_layer(
          __snake_case ,  __snake_case ,  __snake_case ,  __snake_case ,  hf_model.config.feat_extract_norm == """group""" ,  )
      lowercase_								= True
    elif name.split("""."""							)[0] == "proj":
      lowercase_								= fairseq_model.proj
      lowercase_								= True
    else:
      for key, mapped_key in MAPPING.items():
        if key in name or key.split("""w2v_model."""							)[-1] == name.split("""."""							)[0]:
          lowercase_								= True
          if "*" in mapped_key:
            lowercase_								= name.split(__snake_case							)[0].split("""."""							)[-2]
            lowercase_								= mapped_key.replace("""*""" ,  __snake_case							)
          if "weight_g" in name:
            lowercase_								= """weight_g"""
          elif "weight_v" in name:
            lowercase_								= """weight_v"""
          elif "bias" in name:
            lowercase_								= """bias"""
          elif "weight" in name:
            lowercase_								= """weight"""
          else:
            lowercase_								= 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}'''							)

  return proj_weight


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						int:







  '''simple docstring'''


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

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


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						List[str]:







  '''simple docstring'''


  lowercase_					,    lowercase_								= emb.weight.shape
  lowercase_								= nn.Linear(__snake_case ,  __snake_case ,  bias=__snake_case							)
  lowercase_								= emb.weight.data
  return lin_layer


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Union[str, Any]:







  '''simple docstring'''


  with open(__snake_case ,  """r""" ,  encoding="""utf-8"""							) as f:
    lowercase_								= f.readlines()
    lowercase_								= [line.split(""" """							)[0] for line in lines]

  lowercase_								= len(__snake_case							)

  lowercase_								= {
      """<s>""": 0,
      """<pad>""": 1,
      """</s>""": 2,
      """<unk>""": 3,
  }

  vocab_dict.update(dict(zip(__snake_case ,  range(4 ,  num_words + 4							)							)							)							)
  return vocab_dict


@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  ) ->						Dict:







  '''simple docstring'''


  lowercase_								= WavaVecaConfig.from_pretrained(__snake_case							)
  lowercase_								= SpeechaTextaConfig.from_pretrained(
      __snake_case ,  vocab_size=__snake_case ,  decoder_layers=__snake_case ,  do_stable_layer_norm=__snake_case							)

  lowercase_								= WavaVecaFeatureExtractor(
      feature_size=1 ,  sampling_rate=1_60_00 ,  padding_value=0 ,  do_normalize=__snake_case ,  return_attention_mask=__snake_case ,  )

  lowercase_					,    lowercase_					,    lowercase_								= fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path] ,  arg_overrides={"""data""": """/""".join(dict_path.split("""/"""							)[:-1]							)}							)
  lowercase_								= model[0].eval()

  # set weights for wav2vec2 encoder
  lowercase_								= WavaVecaModel(__snake_case							)
  lowercase_								= recursively_load_weights_wavaveca(model.encoder ,  __snake_case							)

  lowercase_								= SpeechaTextaForCausalLM(__snake_case							)
  lowercase_					,    lowercase_								= hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() ,  strict=__snake_case							)

  # set output linear layer
  unexpected_keys.remove("""embed_out"""							)
  lowercase_								= nn.Parameter(model.decoder.embed_out.detach()							)

  # layer norm is init to identity matrix so leaving it is fine
  logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}'''							)
  logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}'''							)

  lowercase_								= SpeechEncoderDecoderModel(encoder=__snake_case ,  decoder=__snake_case							)
  lowercase_								= False

  # add projection layer
  lowercase_								= nn.Parameter(projection_layer.weight							)
  lowercase_								= nn.Parameter(projection_layer.bias							)

  lowercase_								= create_vocab_dict(__snake_case							)

  with open(os.path.join(__snake_case ,  """vocab.json"""							) ,  """w"""							) as fp:
    json.dump(__snake_case ,  __snake_case							)

  lowercase_								= SpeechaTextaTokenizer(os.path.join(__snake_case ,  """vocab.json"""							)							)
  tokenizer.save_pretrained(__snake_case							)

  lowercase_								= hf_wavavec.config.to_dict()
  lowercase_								= tokenizer.pad_token_id
  lowercase_								= tokenizer.bos_token_id
  lowercase_								= tokenizer.eos_token_id
  lowercase_								= """speech_to_text_2"""
  lowercase_								= """wav2vec2"""

  lowercase_								= SpeechEncoderDecoderConfig.from_dict(__snake_case							)

  hf_wavavec.save_pretrained(__snake_case							)
  feature_extractor.save_pretrained(__snake_case							)


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(
           "--encoder_config_path",
           default="facebook/wav2vec2-large-lv60",
           type=str,
           help="Path to hf encoder wav2vec2 checkpoint config",
       )
       parser.add_argument(
           "--decoder_config_path",
           default="facebook/s2t-small-mustc-en-fr-st",
           type=str,
           help="Path to hf decoder s2t checkpoint config",
       )
       parser.add_argument("--vocab_size", default=1_0224, type=int, help="Vocab size of decoder")
       parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

       UpperCAmelCase			= parser.parse_args()
       convert_wavaveca_checkpoint(
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.dict_path,
           encoder_config_path=args.encoder_config_path,
           decoder_config_path=args.decoder_config_path,
           vocab_size=args.vocab_size,
           num_decoder_layers=args.num_decoder_layers,
       )






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase						:		Optional[Any]			= {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		str			= [
           "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "WavLMForAudioFrameClassification",
           "WavLMForCTC",
           "WavLMForSequenceClassification",
           "WavLMForXVector",
           "WavLMModel",
           "WavLMPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_wavlm import (
                  WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  WavLMForAudioFrameClassification,
                  WavLMForCTC,
                  WavLMForSequenceClassification,
                  WavLMForXVector,
                  WavLMModel,
                  WavLMPreTrainedModel,
              )

else:
       import sys

       UpperCAmelCase						:		Optional[Any]			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

