text: # coding=utf-8
  # Copyright 2021 The HuggingFace Inc. team.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """Convert Wav2Vec2 checkpoint."""
  
  
  import argparse
  import json
  import os
  
  import fairseq
  import torch
  from torch import nn
  
  from transformers import (
      Speech2Text2Config,
      Speech2Text2ForCausalLM,
      Speech2Text2Tokenizer,
      SpeechEncoderDecoderConfig,
      SpeechEncoderDecoderModel,
      Wav2Vec2Config,
      Wav2Vec2FeatureExtractor,
      Wav2Vec2Model,
      logging,
  )
  
  
  logging.set_verbosity_info()
  logger = logging.get_logger(__name__)
  
  MAPPING = {
      "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",
  }
  TOP_LEVEL_KEYS = [
      "lm_head",
      "quantizer.weight_proj",
      "quantizer.codevectors",
      "project_q",
      "project_hid",
  ]
  
  
  def set_recursively(hf_pointer, key, value, full_name, weight_type):
      for attribute in key.split("."):
          hf_pointer = getattr(hf_pointer, attribute)
  
      if weight_type is not None:
          hf_shape = getattr(hf_pointer, weight_type).shape
      else:
          hf_shape = 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 {value.shape} for {full_name}"
  
      if weight_type == "weight":
          hf_pointer.weight.data = value
      elif weight_type == "weight_g":
          hf_pointer.weight_g.data = value
      elif weight_type == "weight_v":
          hf_pointer.weight_v.data = value
      elif weight_type == "bias":
          hf_pointer.bias.data = value
      else:
          hf_pointer.data = value
  
      logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.")
  
  
  def recursively_load_weights_wav2vec2(fairseq_model, hf_model):
      unused_weights = []
      fairseq_dict = fairseq_model.state_dict()
  
      feature_extractor = hf_model.feature_extractor
  
      # if encoder has different dim to decoder -> use proj_weight
      proj_weight = None
  
      for name, value in fairseq_dict.items():
          is_used = False
          if "conv_layers" in name:
              load_conv_layer(
                  name,
                  value,
                  feature_extractor,
                  unused_weights,
                  hf_model.config.feat_extract_norm == "group",
              )
              is_used = True
          elif name.split(".")[0] == "proj":
              proj_weight = fairseq_model.proj
              is_used = True
          else:
              for key, mapped_key in MAPPING.items():
                  if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                      is_used = True
                      if "*" in mapped_key:
                          layer_index = name.split(key)[0].split(".")[-2]
                          mapped_key = mapped_key.replace("*", layer_index)
                      if "weight_g" in name:
                          weight_type = "weight_g"
                      elif "weight_v" in name:
                          weight_type = "weight_v"
                      elif "bias" in name:
                          weight_type = "bias"
                      elif "weight" in name:
                          weight_type = "weight"
                      else:
                          weight_type = None
                      set_recursively(hf_model, mapped_key, value, name, weight_type)
                  continue
          if not is_used:
              unused_weights.append(name)
  
      logger.warning(f"Unused weights: {unused_weights}")
  
      return proj_weight
  
  
  def load_conv_layer(full_name, value, feature_extractor, unused_weights, use_group_norm):
      name = full_name.split("conv_layers.")[-1]
      items = name.split(".")
      layer_id = int(items[0])
      type_id = 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 {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
              feature_extractor.conv_layers[layer_id].conv.bias.data = 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 {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
              feature_extractor.conv_layers[layer_id].conv.weight.data = 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."
              feature_extractor.conv_layers[layer_id].layer_norm.bias.data = 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 {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
              feature_extractor.conv_layers[layer_id].layer_norm.weight.data = value
              logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
      else:
          unused_weights.append(full_name)
  
  
  def make_linear_from_emb(emb):
      vocab_size, emb_size = emb.weight.shape
      lin_layer = nn.Linear(vocab_size, emb_size, bias=False)
      lin_layer.weight.data = emb.weight.data
      return lin_layer
  
  
  def create_vocab_dict(dict_path):
      with open(dict_path, "r", encoding="utf-8") as f:
          lines = f.readlines()
          words = [line.split(" ")[0] for line in lines]
  
      num_words = len(words)
  
      vocab_dict = {
          "<s>": 0,
          "<pad>": 1,
          "</s>": 2,
          "<unk>": 3,
      }
  
      vocab_dict.update({k: v for k, v in zip(words, range(4, num_words + 4))})
      return vocab_dict
  
  
  @torch.no_grad()
  def convert_wav2vec2_checkpoint(
      checkpoint_path,
      pytorch_dump_folder_path,
      dict_path,
      encoder_config_path,
      decoder_config_path,
      vocab_size,
      num_decoder_layers,
  ):
      """
      Copy/paste/tweak model's weights to transformers design.
      """
      encoder_config = Wav2Vec2Config.from_pretrained(encoder_config_path)
      decoder_config = Speech2Text2Config.from_pretrained(
          decoder_config_path, vocab_size=vocab_size, decoder_layers=num_decoder_layers, do_stable_layer_norm=True
      )
  
      feature_extractor = Wav2Vec2FeatureExtractor(
          feature_size=1,
          sampling_rate=16000,
          padding_value=0,
          do_normalize=True,
          return_attention_mask=True,
      )
  
      model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path], arg_overrides={"data": "/".join(dict_path.split("/")[:-1])}
      )
      model = model[0].eval()
  
      # set weights for wav2vec2 encoder
      hf_encoder = Wav2Vec2Model(encoder_config)
      projection_layer = recursively_load_weights_wav2vec2(model.encoder, hf_encoder)
  
      hf_decoder = Speech2Text2ForCausalLM(decoder_config)
      missing_keys, unexpected_keys = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(), strict=False)
  
      # set output linear layer
      unexpected_keys.remove("embed_out")
      hf_decoder.lm_head.weight = 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}")
  
      hf_wav2vec = SpeechEncoderDecoderModel(encoder=hf_encoder, decoder=hf_decoder)
      hf_wav2vec.config.tie_word_embeddings = False
  
      # add projection layer
      hf_wav2vec.enc_to_dec_proj.weight = nn.Parameter(projection_layer.weight)
      hf_wav2vec.enc_to_dec_proj.bias = nn.Parameter(projection_layer.bias)
  
      vocab_dict = create_vocab_dict(dict_path)
  
      with open(os.path.join(pytorch_dump_folder_path, "vocab.json"), "w") as fp:
          json.dump(vocab_dict, fp)
  
      tokenizer = Speech2Text2Tokenizer(os.path.join(pytorch_dump_folder_path, "vocab.json"))
      tokenizer.save_pretrained(pytorch_dump_folder_path)
  
      config = hf_wav2vec.config.to_dict()
      config["pad_token_id"] = tokenizer.pad_token_id
      config["bos_token_id"] = tokenizer.bos_token_id
      config["eos_token_id"] = tokenizer.eos_token_id
      config["tokenizer_class"] = "speech_to_text_2"
      config["feature_extractor_type"] = "wav2vec2"
  
      hf_wav2vec.config = SpeechEncoderDecoderConfig.from_dict(config)
  
      hf_wav2vec.save_pretrained(pytorch_dump_folder_path)
      feature_extractor.save_pretrained(pytorch_dump_folder_path)
  
  
  if __name__ == "__main__":
      parser = 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=10224, type=int, help="Vocab size of decoder")
      parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")
  
      args = parser.parse_args()
      convert_wav2vec2_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,
      )
  