






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

__magic_name__   :  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''',
}
__magic_name__   :  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''',
}
__magic_name__   :  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''',
}
__magic_name__   :  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''',
}
__magic_name__   :  List[Any]        =							{
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
__magic_name__   :  Optional[int]        =							{
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
__magic_name__   :  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''',
}
__magic_name__   :  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''',
}
__magic_name__   :  Tuple        =							{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__magic_name__   :  List[str]        =							{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__magic_name__   :  Optional[int]        =							{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__magic_name__   :  int        =							[]
__magic_name__   :  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''',
]
__magic_name__   :  Union[str, Any]        =							IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
__magic_name__   :  Optional[int]        =							IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
__magic_name__   :  str        =							IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			Optional[Any]:
      """simple docstring"""

      for attribute in key.split('.'):
            UpperCamelCase					       =  getattr(_UpperCamelCase       ,							_UpperCamelCase)

      if weight_type is not None:
            UpperCamelCase					       =  getattr(_UpperCamelCase       ,							_UpperCamelCase).shape
      else:
            UpperCamelCase					       =  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					       =  value
      elif weight_type == "weight_g":
            UpperCamelCase					       =  value
      elif weight_type == "weight_v":
            UpperCamelCase					       =  value
      elif weight_type == "bias":
            UpperCamelCase					       =  value
      elif weight_type == "running_mean":
            UpperCamelCase					       =  value
      elif weight_type == "running_var":
            UpperCamelCase					       =  value
      elif weight_type == "num_batches_tracked":
            UpperCamelCase					       =  value
      else:
            UpperCamelCase					       =  value

      logger.info(F'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.')
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			int:
      """simple docstring"""

      for key in ignore_keys:
            if key.endswith('.*'):
                  if name.startswith(key[:-1]):
                        return True
            elif ".*." in key:
                  UpperCamelCase       ,  UpperCamelCase					       =  key.split('.*.')
                  if prefix in name and suffix in name:
                        return True
            elif key in name:
                  return True
      return False
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			str:
      """simple docstring"""

      UpperCamelCase					       =  []

      if task == "s2t":
            UpperCamelCase					       =  hf_model.speechta.encoder.prenet.feature_encoder
            UpperCamelCase					       =  MAPPING_S2T
            UpperCamelCase					       =  IGNORE_KEYS_S2T
      elif task == "t2s":
            UpperCamelCase					       =  None
            UpperCamelCase					       =  MAPPING_T2S
            UpperCamelCase					       =  IGNORE_KEYS_T2S
      elif task == "s2s":
            UpperCamelCase					       =  hf_model.speechta.encoder.prenet.feature_encoder
            UpperCamelCase					       =  MAPPING_S2S
            UpperCamelCase					       =  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					       =  False
            if "conv_layers" in name:
                  load_conv_layer(
                      _UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							hf_model.config.feat_extract_norm == 'group'       ,							)
                  UpperCamelCase					       =  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					       =  key.split('.*.')
                              if prefix in name and suffix in name:
                                    UpperCamelCase					       =  suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                        if key in name:
                              UpperCamelCase					       =  True
                              if "*" in mapped_key:
                                    UpperCamelCase					       =  name.split(_UpperCamelCase)[0].split('.')[-2]
                                    UpperCamelCase					       =  mapped_key.replace('*'       ,							_UpperCamelCase)
                              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'
                              elif "running_mean" in name:
                                    UpperCamelCase					       =  'running_mean'
                              elif "running_var" in name:
                                    UpperCamelCase					       =  'running_var'
                              elif "num_batches_tracked" in name:
                                    UpperCamelCase					       =  'num_batches_tracked'
                              else:
                                    UpperCamelCase					       =  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 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			Tuple:
      """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:
                  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					       =  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					       =  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					       =  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					       =  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 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase=None       ,							_UpperCamelCase=None       ,							_UpperCamelCase=None       ,							) ->			Optional[Any]:
      """simple docstring"""

      if config_path is not None:
            UpperCamelCase					       =  SpeechTaConfig.from_pretrained(_UpperCamelCase)
      else:
            UpperCamelCase					       =  SpeechTaConfig()

      if task == "s2t":
            UpperCamelCase					       =  config.max_text_positions
            UpperCamelCase					       =  SpeechTaForSpeechToText(_UpperCamelCase)
      elif task == "t2s":
            UpperCamelCase					       =  18_76
            UpperCamelCase					       =  6_00
            UpperCamelCase					       =  config.max_speech_positions
            UpperCamelCase					       =  SpeechTaForTextToSpeech(_UpperCamelCase)
      elif task == "s2s":
            UpperCamelCase					       =  18_76
            UpperCamelCase					       =  config.max_speech_positions
            UpperCamelCase					       =  SpeechTaForSpeechToSpeech(_UpperCamelCase)
      else:
            raise ValueError(F'Unknown task name: {task}')

      if vocab_path:
            UpperCamelCase					       =  SpeechTaTokenizer(_UpperCamelCase       ,							model_max_length=config.max_text_positions)

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

      UpperCamelCase					       =  SpeechTaFeatureExtractor()
      UpperCamelCase					       =  SpeechTaProcessor(tokenizer=_UpperCamelCase       ,							feature_extractor=_UpperCamelCase)
      processor.save_pretrained(_UpperCamelCase)

      UpperCamelCase					       =  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__":
       __magic_name__   :  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.'''
       )

       __magic_name__   :  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()



