






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()
UpperCAmelCase_									=							logging.get_logger("transformers.models.speecht5")

UpperCAmelCase_									=							{
    "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",
}
UpperCAmelCase_									=							{
    "text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
    "text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
UpperCAmelCase_									=							{
    "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",
}
UpperCAmelCase_									=							{
    "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",
}
UpperCAmelCase_									=							{
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
UpperCAmelCase_									=							{
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
UpperCAmelCase_									=							{
    "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",
}
UpperCAmelCase_									=							{
    "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",
}
UpperCAmelCase_									=							{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
UpperCAmelCase_									=							{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
UpperCAmelCase_									=							{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
UpperCAmelCase_									=							[]
UpperCAmelCase_									=							[
    "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",
]
UpperCAmelCase_									=							IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
UpperCAmelCase_									=							IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
UpperCAmelCase_									=							IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	List[str]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	Optional[int]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	int				)->Tuple:
      for attribute in key.split('''.'''				):
            _lowerCAmelCase 			=  getattr(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)

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

      logger.info(f'''{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'''				)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Tuple     ,				_SCREAMING_SNAKE_CASE   :	Optional[int]				)->Dict:
      for key in ignore_keys:
            if key.endswith('''.*'''				):
                  if name.startswith(key[:-1]				):
                        return True
            elif ".*." in key:
                  _lowerCAmelCase			,   _lowerCAmelCase 			=  key.split('''.*.'''				)
                  if prefix in name and suffix in name:
                        return True
            elif key in name:
                  return True
      return False
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	List[str]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	List[str]				)->int:
      _lowerCAmelCase 			=  []

      if task == "s2t":
            _lowerCAmelCase 			=  hf_model.speechta.encoder.prenet.feature_encoder
            _lowerCAmelCase 			=  MAPPING_S2T
            _lowerCAmelCase 			=  IGNORE_KEYS_S2T
      elif task == "t2s":
            _lowerCAmelCase 			=  None
            _lowerCAmelCase 			=  MAPPING_T2S
            _lowerCAmelCase 			=  IGNORE_KEYS_T2S
      elif task == "s2s":
            _lowerCAmelCase 			=  hf_model.speechta.encoder.prenet.feature_encoder
            _lowerCAmelCase 			=  MAPPING_S2S
            _lowerCAmelCase 			=  IGNORE_KEYS_S2S
      else:
            raise ValueError(f'''Unsupported task: {task}'''				)

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

            _lowerCAmelCase 			=  False
            if "conv_layers" in name:
                  load_conv_layer(
                      _SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				hf_model.config.feat_extract_norm == '''group'''     ,				)
                  _lowerCAmelCase 			=  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:
                              _lowerCAmelCase			,   _lowerCAmelCase 			=  key.split('''.*.'''				)
                              if prefix in name and suffix in name:
                                    _lowerCAmelCase 			=  suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                        if key in name:
                              _lowerCAmelCase 			=  True
                              if "*" in mapped_key:
                                    _lowerCAmelCase 			=  name.split(_SCREAMING_SNAKE_CASE				)[0].split('''.'''				)[-2]
                                    _lowerCAmelCase 			=  mapped_key.replace('''*'''     ,				_SCREAMING_SNAKE_CASE				)
                              if "weight_g" in name:
                                    _lowerCAmelCase 			=  '''weight_g'''
                              elif "weight_v" in name:
                                    _lowerCAmelCase 			=  '''weight_v'''
                              elif "bias" in name:
                                    _lowerCAmelCase 			=  '''bias'''
                              elif "weight" in name:
                                    _lowerCAmelCase 			=  '''weight'''
                              elif "running_mean" in name:
                                    _lowerCAmelCase 			=  '''running_mean'''
                              elif "running_var" in name:
                                    _lowerCAmelCase 			=  '''running_var'''
                              elif "num_batches_tracked" in name:
                                    _lowerCAmelCase 			=  '''num_batches_tracked'''
                              else:
                                    _lowerCAmelCase 			=  None
                              set_recursively(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)
                        continue
            if not is_used:
                  unused_weights.append(_SCREAMING_SNAKE_CASE				)

      logger.warning(f'''Unused weights: {unused_weights}'''				)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Dict     ,				_SCREAMING_SNAKE_CASE   :	int     ,				_SCREAMING_SNAKE_CASE   :	Optional[int]     ,				_SCREAMING_SNAKE_CASE   :	List[str]     ,				_SCREAMING_SNAKE_CASE   :	str				)->List[str]:
      _lowerCAmelCase 			=  full_name.split('''conv_layers.'''				)[-1]
      _lowerCAmelCase 			=  name.split('''.'''				)
      _lowerCAmelCase 			=  int(items[0]				)
      _lowerCAmelCase 			=  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.'''				)
                  _lowerCAmelCase 			=  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.'''				)
                  _lowerCAmelCase 			=  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.'''				)
                  _lowerCAmelCase 			=  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.'''				)
                  _lowerCAmelCase 			=  value
                  logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
      else:
            unused_weights.append(_SCREAMING_SNAKE_CASE				)
@torch.no_grad()
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	List[str]     ,				_SCREAMING_SNAKE_CASE   :	Union[str, Any]     ,				_SCREAMING_SNAKE_CASE   :	Dict=None     ,				_SCREAMING_SNAKE_CASE   :	Optional[int]=None     ,				_SCREAMING_SNAKE_CASE   :	Tuple=None     ,				)->Optional[Any]:
      if config_path is not None:
            _lowerCAmelCase 			=  SpeechTaConfig.from_pretrained(_SCREAMING_SNAKE_CASE				)
      else:
            _lowerCAmelCase 			=  SpeechTaConfig()

      if task == "s2t":
            _lowerCAmelCase 			=  config.max_text_positions
            _lowerCAmelCase 			=  SpeechTaForSpeechToText(_SCREAMING_SNAKE_CASE				)
      elif task == "t2s":
            _lowerCAmelCase 			=  1_8_7_6
            _lowerCAmelCase 			=  6_0_0
            _lowerCAmelCase 			=  config.max_speech_positions
            _lowerCAmelCase 			=  SpeechTaForTextToSpeech(_SCREAMING_SNAKE_CASE				)
      elif task == "s2s":
            _lowerCAmelCase 			=  1_8_7_6
            _lowerCAmelCase 			=  config.max_speech_positions
            _lowerCAmelCase 			=  SpeechTaForSpeechToSpeech(_SCREAMING_SNAKE_CASE				)
      else:
            raise ValueError(f'''Unknown task name: {task}'''				)

      if vocab_path:
            _lowerCAmelCase 			=  SpeechTaTokenizer(_SCREAMING_SNAKE_CASE     ,				model_max_length=config.max_text_positions				)

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

      _lowerCAmelCase 			=  SpeechTaFeatureExtractor()
      _lowerCAmelCase 			=  SpeechTaProcessor(tokenizer=_SCREAMING_SNAKE_CASE     ,				feature_extractor=_SCREAMING_SNAKE_CASE				)
      processor.save_pretrained(_SCREAMING_SNAKE_CASE				)

      _lowerCAmelCase 			=  torch.load(_SCREAMING_SNAKE_CASE				)
      recursively_load_weights(fairseq_checkpoint['''model''']     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)

      model.save_pretrained(_SCREAMING_SNAKE_CASE				)

      if repo_id:
            print('''Pushing to the hub...'''				)
            processor.push_to_hub(_SCREAMING_SNAKE_CASE				)
            model.push_to_hub(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
    UpperCAmelCase_									=							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."
    )

    UpperCAmelCase_									=							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 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Optional[Any]				)->Any:  # noqa: E741
      _lowerCAmelCase 			=  len(_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  0
      _lowerCAmelCase 			=  [0] * n
      _lowerCAmelCase 			=  [False] * n
      _lowerCAmelCase 			=  [False] * n

      def dfs(_SCREAMING_SNAKE_CASE   :	List[str]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	Optional[int]     ,				_SCREAMING_SNAKE_CASE   :	int				):
            if parent == root:
                  out_edge_count += 1
            _lowerCAmelCase 			=  True
            _lowerCAmelCase 			=  at

            for to in l[at]:
                  if to == parent:
                        pass
                  elif not visited[to]:
                        _lowerCAmelCase 			=  dfs(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)
                        _lowerCAmelCase 			=  min(low[at]     ,				low[to]				)

                        # AP found via bridge
                        if at < low[to]:
                              _lowerCAmelCase 			=  True
                        # AP found via cycle
                        if at == low[to]:
                              _lowerCAmelCase 			=  True
                  else:
                        _lowerCAmelCase 			=  min(low[at]     ,				_SCREAMING_SNAKE_CASE				)
            return out_edge_count

      for i in range(_SCREAMING_SNAKE_CASE				):
            if not visited[i]:
                  _lowerCAmelCase 			=  0
                  _lowerCAmelCase 			=  dfs(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				-1     ,				_SCREAMING_SNAKE_CASE				)
                  _lowerCAmelCase 			=  out_edge_count > 1

      for x in range(len(_SCREAMING_SNAKE_CASE				)				):
            if is_art[x] is True:
                  print(_SCREAMING_SNAKE_CASE				)


# Adjacency list of graph
UpperCAmelCase_									=							{
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}


compute_ap(data)