


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

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


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_  ,       A_	)->				Dict:
  '''simple docstring'''







  for attribute in key.split("."	):
    a						:		List[str]											=      getattr(UpperCAmelCase_  ,       UpperCAmelCase_	)

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

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


def 					lowercase      (    A_  ,       A_	)->				List[Any]:
  '''simple docstring'''







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


def 					lowercase      (    A_  ,       A_  ,       A_	)->				Union[str, Any]:
  '''simple docstring'''







  a						:		List[str]											=      []

  if task == "s2t":
    a						:		Optional[int]											=      hf_model.speechta.encoder.prenet.feature_encoder
    a						:		str											=      MAPPING_S2T
    a						:		str											=      IGNORE_KEYS_S2T
  elif task == "t2s":
    a						:		Dict											=      None
    a						:		Dict											=      MAPPING_T2S
    a						:		Tuple											=      IGNORE_KEYS_T2S
  elif task == "s2s":
    a						:		Dict											=      hf_model.speechta.encoder.prenet.feature_encoder
    a						:		Dict											=      MAPPING_S2S
    a						:		Optional[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

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

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
        if key in name:
          a						:		Optional[int]											=      True
          if "*" in mapped_key:
            a						:		str											=      name.split(UpperCAmelCase_	)[0].split("."	)[-2]
            a						:		Union[str, Any]											=      mapped_key.replace("*"  ,       UpperCAmelCase_	)
          if "weight_g" in name:
            a						:		Any											=      "weight_g"
          elif "weight_v" in name:
            a						:		str											=      "weight_v"
          elif "bias" in name:
            a						:		Optional[Any]											=      "bias"
          elif "weight" in name:
            a						:		List[str]											=      "weight"
          elif "running_mean" in name:
            a						:		Union[str, Any]											=      "running_mean"
          elif "running_var" in name:
            a						:		Optional[int]											=      "running_var"
          elif "num_batches_tracked" in name:
            a						:		Optional[Any]											=      "num_batches_tracked"
          else:
            a						:		List[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 					lowercase      (    A_  ,       A_  ,       A_  ,       A_  ,       A_	)->				List[Any]:
  '''simple docstring'''







  a						:		Union[str, Any]											=      full_name.split("conv_layers."	)[-1]
  a						:		Dict											=      name.split("."	)
  a						:		int											=      int(items[0]	)
  a						:		Tuple											=      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.'''	)
      a						:		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.'''	)
      a						:		Tuple											=      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.'''	)
      a						:		List[Any]											=      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.'''	)
      a						:		Union[str, 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 					lowercase      (    A_  ,       A_  ,       A_  ,       A_=None  ,       A_=None  ,       A_=None  ,       )->				str:
  '''simple docstring'''







  if config_path is not None:
    a						:		Any											=      SpeechTaConfig.from_pretrained(UpperCAmelCase_	)
  else:
    a						:		str											=      SpeechTaConfig()

  if task == "s2t":
    a						:		Dict											=      config.max_text_positions
    a						:		Optional[Any]											=      SpeechTaForSpeechToText(UpperCAmelCase_	)
  elif task == "t2s":
    a						:		Dict											=      1_876
    a						:		Tuple											=      600
    a						:		Union[str, Any]											=      config.max_speech_positions
    a						:		Optional[int]											=      SpeechTaForTextToSpeech(UpperCAmelCase_	)
  elif task == "s2s":
    a						:		Any											=      1_876
    a						:		List[str]											=      config.max_speech_positions
    a						:		int											=      SpeechTaForSpeechToSpeech(UpperCAmelCase_	)
  else:
    raise ValueError(F'''Unknown task name: {task}'''	)

  if vocab_path:
    a						:		Optional[Any]											=      SpeechTaTokenizer(UpperCAmelCase_  ,       model_max_length=config.max_text_positions	)

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

  a						:		List[Any]											=      SpeechTaFeatureExtractor()
  a						:		Any											=      SpeechTaProcessor(tokenizer=UpperCAmelCase_  ,       feature_extractor=UpperCAmelCase_	)
  processor.save_pretrained(UpperCAmelCase_	)

  a						:		Optional[Any]											=      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__":
 __lowercase    =				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."""
 )

 __lowercase    =				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"""







import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


__lowercase    =				logging.get_logger(__name__)






class 						_A  (  _a  ):







    """simple docstring"""




    def __init__( self    :   List[Any]    ,			*__UpperCAmelCase    :   int    ,			**__UpperCAmelCase    :   List[Any]):
      warnings.warn(
          "The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
          " use DPTImageProcessor instead."    ,			__UpperCAmelCase    ,			)
      super().__init__(*__UpperCAmelCase    ,			**__UpperCAmelCase)






