"""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     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  for attribute in key.split("""."""		):
    _UpperCAmelCase						 =      getattr(lowercase   ,lowercase		)

  if weight_type is not None:
    _UpperCAmelCase						 =      getattr(lowercase   ,lowercase		).shape
  else:
    _UpperCAmelCase						 =      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						 =      value
  elif weight_type == "weight_g":
    _UpperCAmelCase						 =      value
  elif weight_type == "weight_v":
    _UpperCAmelCase						 =      value
  elif weight_type == "bias":
    _UpperCAmelCase						 =      value
  else:
    _UpperCAmelCase						 =      value

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






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      []
  _UpperCAmelCase						 =      fairseq_model.state_dict()

  _UpperCAmelCase						 =      hf_model.feature_extractor

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

  for name, value in fairseq_dict.items():
    _UpperCAmelCase						 =      False
    if "conv_layers" in name:
      load_conv_layer(
          lowercase   ,lowercase   ,lowercase   ,lowercase   ,hf_model.config.feat_extract_norm == """group"""   ,)
      _UpperCAmelCase						 =      True
    elif name.split("""."""		)[0] == "proj":
      _UpperCAmelCase						 =      fairseq_model.proj
      _UpperCAmelCase						 =      True
    else:
      for key, mapped_key in MAPPING.items():
        if key in name or key.split("""w2v_model."""		)[-1] == name.split("""."""		)[0]:
          _UpperCAmelCase						 =      True
          if "*" in mapped_key:
            _UpperCAmelCase						 =      name.split(lowercase		)[0].split("""."""		)[-2]
            _UpperCAmelCase						 =      mapped_key.replace("""*"""   ,lowercase		)
          if "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:
            _UpperCAmelCase						 =      """weight"""
          else:
            _UpperCAmelCase						 =      None
          set_recursively(lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase		)
        continue
    if not is_used:
      unused_weights.append(lowercase		)

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

  return proj_weight






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase		):







  """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:
      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						 =      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						 =      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						 =      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						 =      value
      logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
  else:
    unused_weights.append(lowercase		)






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase ,				_UpperCAmelCase						 =      emb.weight.shape
  _UpperCAmelCase						 =      nn.Linear(lowercase   ,lowercase   ,bias=lowercase		)
  _UpperCAmelCase						 =      emb.weight.data
  return lin_layer






def     __UpperCAmelCase					(			lowercase		):







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

  _UpperCAmelCase						 =      len(lowercase		)

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

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






@torch.no_grad()
def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase   ,):







  """simple docstring"""
  _UpperCAmelCase						 =      WavaVecaConfig.from_pretrained(lowercase		)
  _UpperCAmelCase						 =      SpeechaTextaConfig.from_pretrained(
      lowercase   ,vocab_size=lowercase   ,decoder_layers=lowercase   ,do_stable_layer_norm=lowercase		)

  _UpperCAmelCase						 =      WavaVecaFeatureExtractor(
      feature_size=1   ,sampling_rate=1_60_00   ,padding_value=0   ,do_normalize=lowercase   ,return_attention_mask=lowercase   ,)

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

  # set weights for wav2vec2 encoder
  _UpperCAmelCase						 =      WavaVecaModel(lowercase		)
  _UpperCAmelCase						 =      recursively_load_weights_wavaveca(model.encoder   ,lowercase		)

  _UpperCAmelCase						 =      SpeechaTextaForCausalLM(lowercase		)
  _UpperCAmelCase ,				_UpperCAmelCase						 =      hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()   ,strict=lowercase		)

  # set output linear layer
  unexpected_keys.remove("""embed_out"""		)
  _UpperCAmelCase						 =      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						 =      SpeechEncoderDecoderModel(encoder=lowercase   ,decoder=lowercase		)
  _UpperCAmelCase						 =      False

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

  _UpperCAmelCase						 =      create_vocab_dict(lowercase		)

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

  _UpperCAmelCase						 =      SpeechaTextaTokenizer(os.path.join(lowercase   ,"""vocab.json"""		)		)
  tokenizer.save_pretrained(lowercase		)

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

  _UpperCAmelCase						 =      SpeechEncoderDecoderConfig.from_dict(lowercase		)

  hf_wavavec.save_pretrained(lowercase		)
  feature_extractor.save_pretrained(lowercase		)


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_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""")

    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"""


# Lint as: python3
import itertools
import os
import re


UpperCAmelCase__  		=						re.compile(r"""([A-Z]+)([A-Z][a-z])""")
UpperCAmelCase__  		=						re.compile(r"""([a-z\d])([A-Z])""")

UpperCAmelCase__  		=						re.compile(r"""(?<!_)_(?!_)""")
UpperCAmelCase__  		=						re.compile(r"""(_{2,})""")

UpperCAmelCase__  		=						r"""^\w+(\.\w+)*$"""

UpperCAmelCase__  		=						r"""<>:/\|?*"""






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      _uppercase_uppercase_re.sub(R"""\1_\2"""   ,lowercase		)
  _UpperCAmelCase						 =      _lowercase_uppercase_re.sub(R"""\1_\2"""   ,lowercase		)
  return name.lower()






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      _single_underscore_re.split(lowercase		)
  _UpperCAmelCase						 =      [_multiple_underscores_re.split(lowercase		) for n in name]
  return "".join(n.capitalize() for n in itertools.chain.from_iterable(lowercase		) if n != """"""		)






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  if os.path.basename(lowercase		) != name:
    raise ValueError(f'''Should be a dataset name, not a path: {name}'''		)
  return camelcase_to_snakecase(lowercase		)






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """simple docstring"""
  if os.path.basename(lowercase		) != name:
    raise ValueError(f'''Should be a dataset name, not a path: {name}'''		)
  if not re.match(_split_re   ,lowercase		):
    raise ValueError(f'''Split name should match \'{_split_re}\'\' but got \'{split}\'.'''		)
  return f'''{filename_prefix_for_name(lowercase		)}-{split}'''






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase=None		):







  """simple docstring"""
  _UpperCAmelCase						 =      filename_prefix_for_split(lowercase   ,lowercase		)
  if filetype_suffix:
    prefix += f'''.{filetype_suffix}'''
  _UpperCAmelCase						 =      os.path.join(lowercase   ,lowercase		)
  return f'''{filepath}*'''






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase=None   ,lowercase=None		):







  """simple docstring"""
  _UpperCAmelCase						 =      filename_prefix_for_split(lowercase   ,lowercase		)
  _UpperCAmelCase						 =      os.path.join(lowercase   ,lowercase		)

  if shard_lengths:
    _UpperCAmelCase						 =      len(lowercase		)
    _UpperCAmelCase						 =      [f'''{prefix}-{shard_id:05d}-of-{num_shards:05d}''' for shard_id in range(lowercase		)]
    if filetype_suffix:
      _UpperCAmelCase						 =      [filename + f'''.{filetype_suffix}''' for filename in filenames]
    return filenames
  else:
    _UpperCAmelCase						 =      prefix
    if filetype_suffix:
      filename += f'''.{filetype_suffix}'''
    return [filename]
