


import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__					:     Union[str, Any]       =     logging.get_logger('transformers.models.speecht5')

SCREAMING_SNAKE_CASE__					:     Optional[int]       =     {
    '''speech_encoder_prenet.layer_norm''': '''speecht5.encoder.prenet.feature_projection.layer_norm''',
    '''speech_encoder_prenet.post_extract_proj''': '''speecht5.encoder.prenet.feature_projection.projection''',
    '''speech_encoder_prenet.pos_conv.0''': '''speecht5.encoder.prenet.pos_conv_embed.conv''',
    '''speech_encoder_prenet.mask_emb''': '''speecht5.encoder.prenet.masked_spec_embed''',
}
SCREAMING_SNAKE_CASE__					:     List[Any]       =     {
    '''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
    '''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
SCREAMING_SNAKE_CASE__					:     Tuple       =     {
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0''': '''speecht5.decoder.prenet.layers.0''',
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0''': '''speecht5.decoder.prenet.layers.1''',
    '''speech_decoder_prenet.decoder_prenet.0.1''': '''speecht5.decoder.prenet.final_layer''',
    '''speech_decoder_prenet.decoder_prenet.1.alpha''': '''speecht5.decoder.prenet.encode_positions.alpha''',
    '''speech_decoder_prenet.spkembs_layer.0''': '''speecht5.decoder.prenet.speaker_embeds_layer''',
}
SCREAMING_SNAKE_CASE__					:     Union[str, Any]       =     {
    '''speech_decoder_postnet.feat_out''': '''speech_decoder_postnet.feat_out''',
    '''speech_decoder_postnet.prob_out''': '''speech_decoder_postnet.prob_out''',
    '''speech_decoder_postnet.postnet.postnet.0.0''': '''speech_decoder_postnet.layers.0.conv''',
    '''speech_decoder_postnet.postnet.postnet.0.1''': '''speech_decoder_postnet.layers.0.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.1.0''': '''speech_decoder_postnet.layers.1.conv''',
    '''speech_decoder_postnet.postnet.postnet.1.1''': '''speech_decoder_postnet.layers.1.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.2.0''': '''speech_decoder_postnet.layers.2.conv''',
    '''speech_decoder_postnet.postnet.postnet.2.1''': '''speech_decoder_postnet.layers.2.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.3.0''': '''speech_decoder_postnet.layers.3.conv''',
    '''speech_decoder_postnet.postnet.postnet.3.1''': '''speech_decoder_postnet.layers.3.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.4.0''': '''speech_decoder_postnet.layers.4.conv''',
    '''speech_decoder_postnet.postnet.postnet.4.1''': '''speech_decoder_postnet.layers.4.batch_norm''',
}
SCREAMING_SNAKE_CASE__					:     List[Any]       =     {
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
SCREAMING_SNAKE_CASE__					:     Optional[int]       =     {
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
SCREAMING_SNAKE_CASE__					:     int       =     {
    '''encoder.layers.*.self_attn.k_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj''',
    '''encoder.layers.*.self_attn.v_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj''',
    '''encoder.layers.*.self_attn.q_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj''',
    '''encoder.layers.*.self_attn.out_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj''',
    '''encoder.layers.*.self_attn_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.layer_norm''',
    '''encoder.layers.*.fc1''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense''',
    '''encoder.layers.*.fc2''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense''',
    '''encoder.layers.*.final_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''speecht5.encoder.wrapped_encoder.layer_norm''',
    '''encoder.pos_emb.pe_k''': '''speecht5.encoder.wrapped_encoder.embed_positions.pe_k''',
}
SCREAMING_SNAKE_CASE__					:     str       =     {
    '''decoder.layers.*.self_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj''',
    '''decoder.layers.*.self_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj''',
    '''decoder.layers.*.self_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj''',
    '''decoder.layers.*.self_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj''',
    '''decoder.layers.*.self_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm''',
    '''decoder.layers.*.encoder_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj''',
    '''decoder.layers.*.encoder_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj''',
    '''decoder.layers.*.encoder_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj''',
    '''decoder.layers.*.encoder_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj''',
    '''decoder.layers.*.encoder_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm''',
    '''decoder.layers.*.fc1''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense''',
    '''decoder.layers.*.fc2''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense''',
    '''decoder.layers.*.final_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm''',
}
SCREAMING_SNAKE_CASE__					:     Tuple       =     {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
SCREAMING_SNAKE_CASE__					:     List[str]       =     {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
SCREAMING_SNAKE_CASE__					:     Optional[int]       =     {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
SCREAMING_SNAKE_CASE__					:     int       =     []
SCREAMING_SNAKE_CASE__					:     Union[str, Any]       =     [
    '''encoder.version''',
    '''encoder.layers.*.norm_k.weight''',
    '''encoder.layers.*.norm_k.bias''',
    '''decoder.version''',
    '''decoder.layers.*.norm_k.weight''',
    '''decoder.layers.*.norm_k.bias''',
    '''decoder.pos_emb.pe_k''',
    '''speech_encoder_prenet.embed_positions._float_tensor''',
    '''text_decoder_prenet.embed_positions._float_tensor''',
]
SCREAMING_SNAKE_CASE__					:     Union[str, Any]       =     IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
SCREAMING_SNAKE_CASE__					:     Optional[int]       =     IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
SCREAMING_SNAKE_CASE__					:     str       =     IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]




def    a__    (		snake_case__					:							Tuple						,   snake_case__					:							List[Any]						,   snake_case__					:							List[Any]						,   snake_case__					:							Tuple						,   snake_case__					:							Dict     ):

  for attribute in key.split("""."""     ):
    _UpperCAmelCase		:							Union[str, Any]							=     getattr(_UpperCamelCase						,   _UpperCamelCase     )

  if weight_type is not None:
    _UpperCAmelCase		:							Optional[Any]							=     getattr(_UpperCamelCase						,   _UpperCamelCase     ).shape
  else:
    _UpperCAmelCase		:							Tuple							=     hf_pointer.shape

  if hf_shape != value.shape:
    raise ValueError(
        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		:							Tuple							=     value
  elif weight_type == "weight_g":
    _UpperCAmelCase		:							Tuple							=     value
  elif weight_type == "weight_v":
    _UpperCAmelCase		:							List[Any]							=     value
  elif weight_type == "bias":
    _UpperCAmelCase		:							Optional[Any]							=     value
  elif weight_type == "running_mean":
    _UpperCAmelCase		:							List[str]							=     value
  elif weight_type == "running_var":
    _UpperCAmelCase		:							List[str]							=     value
  elif weight_type == "num_batches_tracked":
    _UpperCAmelCase		:							Any							=     value
  else:
    _UpperCAmelCase		:							List[str]							=     value

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




def    a__    (		snake_case__					:							Optional[int]						,   snake_case__					:							List[Any]     ):

  for key in ignore_keys:
    if key.endswith(""".*"""     ):
      if name.startswith(key[:-1]     ):
        return True
    elif ".*." in key:
      _UpperCAmelCase,_UpperCAmelCase		:							int							=     key.split(""".*."""     )
      if prefix in name and suffix in name:
        return True
    elif key in name:
      return True
  return False




def    a__    (		snake_case__					:							List[Any]						,   snake_case__					:							List[str]						,   snake_case__					:							Any     ):
  _UpperCAmelCase		:							str							=     []

  if task == "s2t":
    _UpperCAmelCase		:							List[Any]							=     hf_model.speechta.encoder.prenet.feature_encoder
    _UpperCAmelCase		:							int							=     MAPPING_S2T
    _UpperCAmelCase		:							List[Any]							=     IGNORE_KEYS_S2T
  elif task == "t2s":
    _UpperCAmelCase		:							Any							=     None
    _UpperCAmelCase		:							Any							=     MAPPING_T2S
    _UpperCAmelCase		:							Any							=     IGNORE_KEYS_T2S
  elif task == "s2s":
    _UpperCAmelCase		:							Optional[int]							=     hf_model.speechta.encoder.prenet.feature_encoder
    _UpperCAmelCase		:							Tuple							=     MAPPING_S2S
    _UpperCAmelCase		:							Union[str, Any]							=     IGNORE_KEYS_S2S
  else:
    raise ValueError(f'''Unsupported task: {task}'''     )

  for name, value in fairseq_dict.items():
    if should_ignore(_UpperCamelCase						,   _UpperCamelCase     ):
      logger.info(f'''{name} was ignored'''     )
      continue

    _UpperCAmelCase		:							str							=     False
    if "conv_layers" in name:
      load_conv_layer(
          _UpperCamelCase						,   _UpperCamelCase						,   _UpperCamelCase						,   _UpperCamelCase						,   hf_model.config.feat_extract_norm == """group"""						,   )
      _UpperCAmelCase		:							Union[str, Any]							=     True
    else:
      for key, mapped_key in MAPPING.items():
        # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

        if "*" in key:
          _UpperCAmelCase,_UpperCAmelCase		:							str							=     key.split(""".*."""     )
          if prefix in name and suffix in name:
            _UpperCAmelCase		:							str							=     suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
        if key in name:
          _UpperCAmelCase		:							str							=     True
          if "*" in mapped_key:
            _UpperCAmelCase		:							Tuple							=     name.split(_UpperCamelCase     )[0].split("""."""     )[-2]
            _UpperCAmelCase		:							Optional[int]							=     mapped_key.replace("""*"""						,   _UpperCamelCase     )
          if "weight_g" in name:
            _UpperCAmelCase		:							List[str]							=     """weight_g"""
          elif "weight_v" in name:
            _UpperCAmelCase		:							str							=     """weight_v"""
          elif "bias" in name:
            _UpperCAmelCase		:							Optional[int]							=     """bias"""
          elif "weight" in name:
            _UpperCAmelCase		:							Tuple							=     """weight"""
          elif "running_mean" in name:
            _UpperCAmelCase		:							List[str]							=     """running_mean"""
          elif "running_var" in name:
            _UpperCAmelCase		:							Dict							=     """running_var"""
          elif "num_batches_tracked" in name:
            _UpperCAmelCase		:							List[Any]							=     """num_batches_tracked"""
          else:
            _UpperCAmelCase		:							Any							=     None
          set_recursively(_UpperCamelCase						,   _UpperCamelCase						,   _UpperCamelCase						,   _UpperCamelCase						,   _UpperCamelCase     )
        continue
    if not is_used:
      unused_weights.append(_UpperCamelCase     )

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




def    a__    (		snake_case__					:							int						,   snake_case__					:							Any						,   snake_case__					:							Optional[int]						,   snake_case__					:							Optional[Any]						,   snake_case__					:							Any     ):
  _UpperCAmelCase		:							List[Any]							=     full_name.split("""conv_layers."""     )[-1]
  _UpperCAmelCase		:							Dict							=     name.split("""."""     )
  _UpperCAmelCase		:							Any							=     int(items[0]     )
  _UpperCAmelCase		:							Union[str, Any]							=     int(items[1]     )

  if type_id == 0:
    if "bias" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
        raise ValueError(
            f'''{full_name} has size {value.shape}, but'''
            f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''     )
      _UpperCAmelCase		:							Optional[Any]							=     value
      logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''     )
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
        raise ValueError(
            f'''{full_name} has size {value.shape}, but'''
            f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''     )
      _UpperCAmelCase		:							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:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
        raise ValueError(
            f'''{full_name} has size {value.shape}, but'''
            f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''     )
      _UpperCAmelCase		:							str							=     value
      logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
        raise ValueError(
            f'''{full_name} has size {value.shape}, but'''
            f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''     )
      _UpperCAmelCase		:							Optional[Any]							=     value
      logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
  else:
    unused_weights.append(_UpperCamelCase     )




@torch.no_grad()
def    a__    (		snake_case__					:							Any						,   snake_case__					:							Optional[Any]						,   snake_case__					:							Dict						,   snake_case__					:							List[Any]=None						,   snake_case__					:							Union[str, Any]=None						,   snake_case__					:							List[Any]=None						,   ):

  if config_path is not None:
    _UpperCAmelCase		:							Optional[int]							=     SpeechTaConfig.from_pretrained(_UpperCamelCase     )
  else:
    _UpperCAmelCase		:							int							=     SpeechTaConfig()

  if task == "s2t":
    _UpperCAmelCase		:							int							=     config.max_text_positions
    _UpperCAmelCase		:							Optional[Any]							=     SpeechTaForSpeechToText(_UpperCamelCase     )
  elif task == "t2s":
    _UpperCAmelCase		:							List[str]							=     1876
    _UpperCAmelCase		:							Optional[Any]							=     600
    _UpperCAmelCase		:							Any							=     config.max_speech_positions
    _UpperCAmelCase		:							Dict							=     SpeechTaForTextToSpeech(_UpperCamelCase     )
  elif task == "s2s":
    _UpperCAmelCase		:							Tuple							=     1876
    _UpperCAmelCase		:							Optional[Any]							=     config.max_speech_positions
    _UpperCAmelCase		:							Optional[Any]							=     SpeechTaForSpeechToSpeech(_UpperCamelCase     )
  else:
    raise ValueError(f'''Unknown task name: {task}'''     )

  if vocab_path:
    _UpperCAmelCase		:							List[str]							=     SpeechTaTokenizer(_UpperCamelCase						,   model_max_length=config.max_text_positions     )

    # Mask token behaves like a normal word, i.e. include the space before it
    _UpperCAmelCase		:							Optional[int]							=     AddedToken("""<mask>"""						,   lstrip=_UpperCamelCase						,   rstrip=_UpperCamelCase     )
    _UpperCAmelCase		:							List[str]							=     mask_token
    tokenizer.add_special_tokens({"""mask_token""": mask_token}     )
    tokenizer.add_tokens(["""<ctc_blank>"""]     )

  _UpperCAmelCase		:							Optional[Any]							=     SpeechTaFeatureExtractor()
  _UpperCAmelCase		:							Tuple							=     SpeechTaProcessor(tokenizer=_UpperCamelCase						,   feature_extractor=_UpperCamelCase     )
  processor.save_pretrained(_UpperCamelCase     )

  _UpperCAmelCase		:							List[str]							=     torch.load(_UpperCamelCase     )
  recursively_load_weights(fairseq_checkpoint["""model"""]						,   _UpperCamelCase						,   _UpperCamelCase     )

  model.save_pretrained(_UpperCamelCase     )

  if repo_id:
    print("""Pushing to the hub..."""     )
    processor.push_to_hub(_UpperCamelCase     )
    model.push_to_hub(_UpperCamelCase     )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__					:     List[str]       =     argparse.ArgumentParser()
 parser.add_argument(
     '--task',
     default='s2t',
     type=str,
     help='Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.',
 )
 parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to fairseq checkpoint')
 parser.add_argument('--vocab_path', default=None, type=str, help='Path to SentencePiece model')
 parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
 parser.add_argument(
     '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
 )
 parser.add_argument(
     '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
 )

 SCREAMING_SNAKE_CASE__					:     Dict       =     parser.parse_args()
 convert_speechta_checkpoint(
     args.task,
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.vocab_path,
     args.push_to_hub,
 )













def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			bool:
      """simple docstring"""

      return numa ^ numa < 0


if __name__ == "__main__":
       import doctest

       doctest.testmod()



