




"""simple docstring"""


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()
A_      			=       logging.get_logger('''transformers.models.speecht5''')

A_      			=       {
    '''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''',
}
A_      			=       {
    '''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
    '''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
A_      			=       {
    '''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''',
}
A_      			=       {
    '''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''',
}
A_      			=       {
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
A_      			=       {
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
A_      			=       {
    '''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''',
}
A_      			=       {
    '''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''',
}
A_      			=       {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
A_      			=       {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
A_      			=       {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
A_      			=       []
A_      			=       [
    '''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''',
]
A_      			=       IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
A_      			=       IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
A_      			=       IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]


def       UpperCAmelCase__ (snake_case__							:      List[str]	,     snake_case__							:      Optional[Any]	,     snake_case__							:      Any	,     snake_case__							:      Tuple	,     snake_case__							:      Tuple					):
  """simple docstring"""

  for attribute in key.split("""."""					):
    _snake_case							:	List[Any]			=						getattr(snake_case__	,     snake_case__					)

  if weight_type is not None:
    _snake_case							:	Optional[Any]			=						getattr(snake_case__	,     snake_case__					).shape
  else:
    _snake_case							:	List[str]			=						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":
    _snake_case							:	Optional[Any]			=						value
  elif weight_type == "weight_g":
    _snake_case							:	List[str]			=						value
  elif weight_type == "weight_v":
    _snake_case							:	int			=						value
  elif weight_type == "bias":
    _snake_case							:	Tuple			=						value
  elif weight_type == "running_mean":
    _snake_case							:	Optional[Any]			=						value
  elif weight_type == "running_var":
    _snake_case							:	Optional[Any]			=						value
  elif weight_type == "num_batches_tracked":
    _snake_case							:	Optional[Any]			=						value
  else:
    _snake_case							:	int			=						value

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


def       UpperCAmelCase__ (snake_case__							:      Any	,     snake_case__							:      int					):
  """simple docstring"""

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


def       UpperCAmelCase__ (snake_case__							:      Dict	,     snake_case__							:      int	,     snake_case__							:      Any					):
  """simple docstring"""

  _snake_case							:	Tuple			=						[]

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

  for name, value in fairseq_dict.items():
    if should_ignore(snake_case__	,     snake_case__					):
      logger.info(F"{name} was ignored"					)
      continue

    _snake_case							:	Optional[Any]			=						False
    if "conv_layers" in name:
      load_conv_layer(
          snake_case__	,     snake_case__	,     snake_case__	,     snake_case__	,     hf_model.config.feat_extract_norm == """group"""	,     )
      _snake_case							:	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:
          _snake_case	,      _snake_case							:	Union[str, Any]			=						key.split(""".*."""					)
          if prefix in name and suffix in name:
            _snake_case							:	Optional[int]			=						suffix

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

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


def       UpperCAmelCase__ (snake_case__							:      Union[str, Any]	,     snake_case__							:      List[str]	,     snake_case__							:      str	,     snake_case__							:      Optional[int]	,     snake_case__							:      Dict					):
  """simple docstring"""

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


@torch.no_grad()
def       UpperCAmelCase__ (snake_case__							:      Tuple	,     snake_case__							:      int	,     snake_case__							:      Any	,     snake_case__							:      List[Any]=None	,     snake_case__							:      Tuple=None	,     snake_case__							:      Dict=None	,     ):
  """simple docstring"""

  if config_path is not None:
    _snake_case							:	List[Any]			=						SpeechTaConfig.from_pretrained(snake_case__					)
  else:
    _snake_case							:	List[Any]			=						SpeechTaConfig()

  if task == "s2t":
    _snake_case							:	Union[str, Any]			=						config.max_text_positions
    _snake_case							:	Optional[Any]			=						SpeechTaForSpeechToText(snake_case__					)
  elif task == "t2s":
    _snake_case							:	List[Any]			=						18_76
    _snake_case							:	Tuple			=						6_00
    _snake_case							:	Union[str, Any]			=						config.max_speech_positions
    _snake_case							:	Dict			=						SpeechTaForTextToSpeech(snake_case__					)
  elif task == "s2s":
    _snake_case							:	List[str]			=						18_76
    _snake_case							:	List[str]			=						config.max_speech_positions
    _snake_case							:	Dict			=						SpeechTaForSpeechToSpeech(snake_case__					)
  else:
    raise ValueError(F"Unknown task name: {task}"					)

  if vocab_path:
    _snake_case							:	str			=						SpeechTaTokenizer(snake_case__	,     model_max_length=config.max_text_positions					)

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

  _snake_case							:	List[Any]			=						SpeechTaFeatureExtractor()
  _snake_case							:	Optional[Any]			=						SpeechTaProcessor(tokenizer=snake_case__	,     feature_extractor=snake_case__					)
  processor.save_pretrained(snake_case__					)

  _snake_case							:	Optional[Any]			=						torch.load(snake_case__					)
  recursively_load_weights(fairseq_checkpoint["""model"""]	,     snake_case__	,     snake_case__					)

  model.save_pretrained(snake_case__					)

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


if __name__ == "__main__":
     A_      			=       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.'''
     )

     A_      			=       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,
     )








"""simple docstring"""


def       UpperCAmelCase__ (snake_case__							:      int					):
  """simple docstring"""

  if not isinstance(snake_case__	,     snake_case__					) or number < 0:
    raise ValueError("""Input must be a non-negative integer"""					)

  _snake_case							:	Dict			=						0
  while number:
    # This way we arrive at next set bit (next 1) instead of looping
    # through each bit and checking for 1s hence the
    # loop won't run 32 times it will only run the number of `1` times
    number &= number - 1
    count += 1
  return count


if __name__ == "__main__":
     import doctest

     doctest.testmod()


