





"""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()
lowercase__				:Dict				   =       logging.get_logger(__name__)

lowercase__				:List[Any]				   =       {
    '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',
}
lowercase__				:Optional[Any]				   =       [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Tuple:





  """simple docstring"""


  for attribute in key.split('''.'''  ):
    __UpperCAmelCase    :						List[Any]       =						getattr(UpperCAmelCase_  ,			UpperCAmelCase_  )

  if weight_type is not None:
    __UpperCAmelCase    :						Any       =						getattr(UpperCAmelCase_  ,			UpperCAmelCase_  ).shape
  else:
    __UpperCAmelCase    :						Dict       =						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":
    __UpperCAmelCase    :						Any       =						value
  elif weight_type == "weight_g":
    __UpperCAmelCase    :						Optional[int]       =						value
  elif weight_type == "weight_v":
    __UpperCAmelCase    :						int       =						value
  elif weight_type == "bias":
    __UpperCAmelCase    :						Any       =						value
  else:
    __UpperCAmelCase    :						str       =						value

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


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->Optional[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						Tuple       =						[]
  __UpperCAmelCase    :						Optional[Any]       =						fairseq_model.state_dict()

  __UpperCAmelCase    :						int       =						hf_model.feature_extractor

  # if encoder has different dim to decoder -> use proj_weight
  __UpperCAmelCase    :						Tuple       =						None

  for name, value in fairseq_dict.items():
    __UpperCAmelCase    :						Optional[Any]       =						False
    if "conv_layers" in name:
      load_conv_layer(
          UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			hf_model.config.feat_extract_norm == '''group'''  ,			)
      __UpperCAmelCase    :						int       =						True
    elif name.split('''.'''  )[0] == "proj":
      __UpperCAmelCase    :						Tuple       =						fairseq_model.proj
      __UpperCAmelCase    :						Dict       =						True
    else:
      for key, mapped_key in MAPPING.items():
        if key in name or key.split('''w2v_model.'''  )[-1] == name.split('''.'''  )[0]:
          __UpperCAmelCase    :						Union[str, Any]       =						True
          if "*" in mapped_key:
            __UpperCAmelCase    :						int       =						name.split(UpperCAmelCase_  )[0].split('''.'''  )[-2]
            __UpperCAmelCase    :						Dict       =						mapped_key.replace('''*'''  ,			UpperCAmelCase_  )
          if "weight_g" in name:
            __UpperCAmelCase    :						Optional[Any]       =						'''weight_g'''
          elif "weight_v" in name:
            __UpperCAmelCase    :						str       =						'''weight_v'''
          elif "bias" in name:
            __UpperCAmelCase    :						Optional[Any]       =						'''bias'''
          elif "weight" in name:
            __UpperCAmelCase    :						List[str]       =						'''weight'''
          else:
            __UpperCAmelCase    :						Dict       =						None
          set_recursively(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
        continue
    if not is_used:
      unused_weights.append(UpperCAmelCase_  )

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

  return proj_weight


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Any:





  """simple docstring"""


  __UpperCAmelCase    :						int       =						full_name.split('''conv_layers.'''  )[-1]
  __UpperCAmelCase    :						Dict       =						name.split('''.'''  )
  __UpperCAmelCase    :						Tuple       =						int(items[0]  )
  __UpperCAmelCase    :						Union[str, Any]       =						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.'''
      )
      __UpperCAmelCase    :						Tuple       =						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.'''
      )
      __UpperCAmelCase    :						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:
      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."
      )
      __UpperCAmelCase    :						List[Any]       =						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.'''
      )
      __UpperCAmelCase    :						Union[str, Any]       =						value
      logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
  else:
    unused_weights.append(UpperCAmelCase_  )


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Optional[int]:





  """simple docstring"""


  __UpperCAmelCase       ,     __UpperCAmelCase    :						str       =						emb.weight.shape
  __UpperCAmelCase    :						Any       =						nn.Linear(UpperCAmelCase_  ,			UpperCAmelCase_  ,			bias=UpperCAmelCase_  )
  __UpperCAmelCase    :						str       =						emb.weight.data
  return lin_layer


def       lowerCamelCase_       (   UpperCAmelCase_  )						->List[str]:





  """simple docstring"""


  with open(UpperCAmelCase_  ,			'''r'''  ,			encoding='''utf-8'''  ) as f:
    __UpperCAmelCase    :						Optional[int]       =						f.readlines()
    __UpperCAmelCase    :						int       =						[line.split(''' '''  )[0] for line in lines]

  __UpperCAmelCase    :						List[str]       =						len(UpperCAmelCase_  )

  __UpperCAmelCase    :						Tuple       =						{
      '''<s>''': 0,
      '''<pad>''': 1,
      '''</s>''': 2,
      '''<unk>''': 3,
  }

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


@torch.no_grad()
def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			)						->int:





  """simple docstring"""


  __UpperCAmelCase    :						str       =						WavaVecaConfig.from_pretrained(UpperCAmelCase_  )
  __UpperCAmelCase    :						str       =						SpeechaTextaConfig.from_pretrained(
      UpperCAmelCase_  ,			vocab_size=UpperCAmelCase_  ,			decoder_layers=UpperCAmelCase_  ,			do_stable_layer_norm=UpperCAmelCase_  )

  __UpperCAmelCase    :						Optional[Any]       =						WavaVecaFeatureExtractor(
      feature_size=1  ,			sampling_rate=1_60_00  ,			padding_value=0  ,			do_normalize=UpperCAmelCase_  ,			return_attention_mask=UpperCAmelCase_  ,			)

  __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase    :						List[str]       =						fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]  ,			arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''  )[:-1]  )}  )
  __UpperCAmelCase    :						Optional[Any]       =						model[0].eval()

  # set weights for wav2vec2 encoder
  __UpperCAmelCase    :						str       =						WavaVecaModel(UpperCAmelCase_  )
  __UpperCAmelCase    :						Dict       =						recursively_load_weights_wavaveca(model.encoder  ,			UpperCAmelCase_  )

  __UpperCAmelCase    :						List[str]       =						SpeechaTextaForCausalLM(UpperCAmelCase_  )
  __UpperCAmelCase       ,     __UpperCAmelCase    :						Dict       =						hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()  ,			strict=UpperCAmelCase_  )

  # set output linear layer
  unexpected_keys.remove('''embed_out'''  )
  __UpperCAmelCase    :						int       =						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}'''  )

  __UpperCAmelCase    :						Optional[Any]       =						SpeechEncoderDecoderModel(encoder=UpperCAmelCase_  ,			decoder=UpperCAmelCase_  )
  __UpperCAmelCase    :						str       =						False

  # add projection layer
  __UpperCAmelCase    :						int       =						nn.Parameter(projection_layer.weight  )
  __UpperCAmelCase    :						Optional[int]       =						nn.Parameter(projection_layer.bias  )

  __UpperCAmelCase    :						Optional[Any]       =						create_vocab_dict(UpperCAmelCase_  )

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

  __UpperCAmelCase    :						int       =						SpeechaTextaTokenizer(os.path.join(UpperCAmelCase_  ,			'''vocab.json'''  )  )
  tokenizer.save_pretrained(UpperCAmelCase_  )

  __UpperCAmelCase    :						Tuple       =						hf_wavavec.config.to_dict()
  __UpperCAmelCase    :						Any       =						tokenizer.pad_token_id
  __UpperCAmelCase    :						List[Any]       =						tokenizer.bos_token_id
  __UpperCAmelCase    :						str       =						tokenizer.eos_token_id
  __UpperCAmelCase    :						Tuple       =						'''speech_to_text_2'''
  __UpperCAmelCase    :						List[str]       =						'''wav2vec2'''

  __UpperCAmelCase    :						Any       =						SpeechEncoderDecoderConfig.from_dict(UpperCAmelCase_  )

  hf_wavavec.save_pretrained(UpperCAmelCase_  )
  feature_extractor.save_pretrained(UpperCAmelCase_  )


if __name__ == "__main__":
  lowercase__				:Union[str, 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(
      '--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_0_2_2_4, type=int, help='Vocab size of decoder')
  parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

  lowercase__				:Tuple				   =       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"""





# flake8: noqa
# Lint as: python3
lowercase__				:Tuple				   =       [
    'VerificationMode',
    'Version',
    'disable_progress_bar',
    'enable_progress_bar',
    'is_progress_bar_enabled',
    'experimental',
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental