





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()
__lowerCamelCase	:					str			  =					logging.get_logger(__name__)

__lowerCamelCase	:					Optional[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""",
}
__lowerCamelCase	:					Tuple			  =					[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			List[str]:
 for attribute in key.split("."					):
  UpperCamelCase     : Dict	     =       getattr(_lowerCAmelCase							,       _lowerCAmelCase					)

 if weight_type is not None:
  UpperCamelCase     : Optional[int]	     =       getattr(_lowerCAmelCase							,       _lowerCAmelCase					).shape
 else:
  UpperCamelCase     : Optional[Any]	     =       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     : Optional[Any]	     =       value
 elif weight_type == "weight_g":
  UpperCamelCase     : List[Any]	     =       value
 elif weight_type == "weight_v":
  UpperCamelCase     : List[Any]	     =       value
 elif weight_type == "bias":
  UpperCamelCase     : Any	     =       value
 else:
  UpperCamelCase     : Optional[int]	     =       value

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



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			Any:
 UpperCamelCase     : Union[str, Any]	     =       []
 UpperCamelCase     : Optional[int]	     =       fairseq_model.state_dict()

 UpperCamelCase     : str	     =       hf_model.feature_extractor

 # if encoder has different dim to decoder -> use proj_weight
 UpperCamelCase     : str	     =       None

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

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

 return proj_weight



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			str:
 UpperCamelCase     : Union[str, Any]	     =       full_name.split("conv_layers."					)[-1]
 UpperCamelCase     : Tuple	     =       name.split("."					)
 UpperCamelCase     : Optional[int]	     =       int(items[0]					)
 UpperCamelCase     : str	     =       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     : List[str]	     =       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     : List[str]	     =       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     : str	     =       value
   logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
 else:
  unused_weights.append(_lowerCAmelCase					)



def 		A_    (					_lowerCAmelCase					)						->			str:
 UpperCamelCase     ,   UpperCamelCase     : List[Any]	     =       emb.weight.shape
 UpperCamelCase     : Any	     =       nn.Linear(_lowerCAmelCase							,       _lowerCAmelCase							,       bias=_lowerCAmelCase					)
 UpperCamelCase     : Union[str, Any]	     =       emb.weight.data
 return lin_layer



def 		A_    (					_lowerCAmelCase					)						->			Optional[Any]:
 with open(_lowerCAmelCase							,       "r"							,       encoding="utf-8"					) as f:
  UpperCamelCase     : List[str]	     =       f.readlines()
  UpperCamelCase     : List[Any]	     =       [line.split(" "					)[0] for line in lines]

 UpperCamelCase     : Union[str, Any]	     =       len(_lowerCAmelCase					)

 UpperCamelCase     : Union[str, Any]	     =       {
     "<s>": 0,
     "<pad>": 1,
     "</s>": 2,
     "<unk>": 3,
 }

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



@torch.no_grad()
def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       )						->			List[str]:
 UpperCamelCase     : List[str]	     =       WavaVecaConfig.from_pretrained(_lowerCAmelCase					)
 UpperCamelCase     : Tuple	     =       SpeechaTextaConfig.from_pretrained(
     _lowerCAmelCase							,       vocab_size=_lowerCAmelCase							,       decoder_layers=_lowerCAmelCase							,       do_stable_layer_norm=_lowerCAmelCase					)

 UpperCamelCase     : Union[str, Any]	     =       WavaVecaFeatureExtractor(
     feature_size=1							,       sampling_rate=1_6000							,       padding_value=0							,       do_normalize=_lowerCAmelCase							,       return_attention_mask=_lowerCAmelCase							,       )

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

 # set weights for wav2vec2 encoder
 UpperCamelCase     : Union[str, Any]	     =       WavaVecaModel(_lowerCAmelCase					)
 UpperCamelCase     : int	     =       recursively_load_weights_wavaveca(model.encoder							,       _lowerCAmelCase					)

 UpperCamelCase     : Tuple	     =       SpeechaTextaForCausalLM(_lowerCAmelCase					)
 UpperCamelCase     ,   UpperCamelCase     : Tuple	     =       hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()							,       strict=_lowerCAmelCase					)

 # 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     : str	     =       SpeechEncoderDecoderModel(encoder=_lowerCAmelCase							,       decoder=_lowerCAmelCase					)
 UpperCamelCase     : Optional[Any]	     =       False

 # add projection layer
 UpperCamelCase     : Optional[Any]	     =       nn.Parameter(projection_layer.weight					)
 UpperCamelCase     : List[Any]	     =       nn.Parameter(projection_layer.bias					)

 UpperCamelCase     : List[str]	     =       create_vocab_dict(_lowerCAmelCase					)

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

 UpperCamelCase     : int	     =       SpeechaTextaTokenizer(os.path.join(_lowerCAmelCase							,       "vocab.json"					)					)
 tokenizer.save_pretrained(_lowerCAmelCase					)

 UpperCamelCase     : Union[str, Any]	     =       hf_wavavec.config.to_dict()
 UpperCamelCase     : Optional[Any]	     =       tokenizer.pad_token_id
 UpperCamelCase     : Any	     =       tokenizer.bos_token_id
 UpperCamelCase     : Dict	     =       tokenizer.eos_token_id
 UpperCamelCase     : Union[str, Any]	     =       "speech_to_text_2"
 UpperCamelCase     : List[str]	     =       "wav2vec2"

 UpperCamelCase     : Optional[int]	     =       SpeechEncoderDecoderConfig.from_dict(_lowerCAmelCase					)

 hf_wavavec.save_pretrained(_lowerCAmelCase					)
 feature_extractor.save_pretrained(_lowerCAmelCase					)


if __name__ == "__main__":
    __lowerCamelCase	:					Optional[int]			  =					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""")

    __lowerCamelCase	:					Optional[int]			  =					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,
    )







__lowerCamelCase	:					Tuple			  =					"""ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""



def 		A_    (					_lowerCAmelCase					)						->			bytes:
 # Make sure the supplied data is a bytes-like object
 if not isinstance(_lowerCAmelCase							,       _lowerCAmelCase					):
  UpperCamelCase     : str	     =       F"""a bytes-like object is required, not '{data.__class__.__name__}'"""
  raise TypeError(_lowerCAmelCase					)

 UpperCamelCase     : Optional[int]	     =       "".join(bin(_lowerCAmelCase					)[2:].zfill(8					) for byte in data					)

 UpperCamelCase     : str	     =       len(_lowerCAmelCase					) % 6 != 0

 if padding_needed:
  # The padding that will be added later
  UpperCamelCase     : Optional[Any]	     =       b"=" * ((6 - len(_lowerCAmelCase					) % 6) // 2)

  # Append binary_stream with arbitrary binary digits (0's by default) to make its
  # length a multiple of 6.
  binary_stream += "0" * (6 - len(_lowerCAmelCase					) % 6)
 else:
  UpperCamelCase     : List[Any]	     =       b""

 # Encode every 6 binary digits to their corresponding Base64 character
 return (
     "".join(
         B64_CHARSET[int(binary_stream[index : index + 6]							,       2					)]
         for index in range(0							,       len(_lowerCAmelCase					)							,       6					)					).encode()
     + padding
 )



def 		A_    (					_lowerCAmelCase					)						->			bytes:
 # Make sure encoded_data is either a string or a bytes-like object
 if not isinstance(_lowerCAmelCase							,       _lowerCAmelCase					) and not isinstance(_lowerCAmelCase							,       _lowerCAmelCase					):
  UpperCamelCase     : List[Any]	     =       (
      "argument should be a bytes-like object or ASCII string, "
      F"""not '{encoded_data.__class__.__name__}'"""
  )
  raise TypeError(_lowerCAmelCase					)

 # In case encoded_data is a bytes-like object, make sure it contains only
 # ASCII characters so we convert it to a string object
 if isinstance(_lowerCAmelCase							,       _lowerCAmelCase					):
  try:
   UpperCamelCase     : Any	     =       encoded_data.decode("utf-8"					)
  except UnicodeDecodeError:
   raise ValueError("base64 encoded data should only contain ASCII characters"					)

 UpperCamelCase     : Union[str, Any]	     =       encoded_data.count("="					)

 # Check if the encoded string contains non base64 characters
 if padding:
  assert all(
      char in B64_CHARSET for char in encoded_data[:-padding]					), "Invalid base64 character(s) found."
 else:
  assert all(
      char in B64_CHARSET for char in encoded_data					), "Invalid base64 character(s) found."

 # Check the padding
 assert len(_lowerCAmelCase					) % 4 == 0 and padding < 3, "Incorrect padding"

 if padding:
  # Remove padding if there is one
  UpperCamelCase     : List[str]	     =       encoded_data[:-padding]

  UpperCamelCase     : List[Any]	     =       "".join(
      bin(B64_CHARSET.index(_lowerCAmelCase					)					)[2:].zfill(6					) for char in encoded_data					)[: -padding * 2]
 else:
  UpperCamelCase     : List[Any]	     =       "".join(
      bin(B64_CHARSET.index(_lowerCAmelCase					)					)[2:].zfill(6					) for char in encoded_data					)

 UpperCamelCase     : Any	     =       [
     int(binary_stream[index : index + 8]							,       2					)
     for index in range(0							,       len(_lowerCAmelCase					)							,       8					)
 ]

 return bytes(_lowerCAmelCase					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
