





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       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Any:
  for attribute in key.split('.'		):
    lowercase__           =  getattr(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)

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

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




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       List[str]:
  for key in ignore_keys:
    if key.endswith('.*'		):
      if name.startswith(key[:-1]		):
        return True
    elif ".*." in key:
      lowercase__      ,   lowercase__           =  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		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       str:
  lowercase__           =  []

  if task == "s2t":
    lowercase__           =  hf_model.speechta.encoder.prenet.feature_encoder
    lowercase__           =  MAPPING_S2T
    lowercase__           =  IGNORE_KEYS_S2T
  elif task == "t2s":
    lowercase__           =  None
    lowercase__           =  MAPPING_T2S
    lowercase__           =  IGNORE_KEYS_T2S
  elif task == "s2s":
    lowercase__           =  hf_model.speechta.encoder.prenet.feature_encoder
    lowercase__           =  MAPPING_S2S
    lowercase__           =  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

    lowercase__           =  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'		,						)
      lowercase__           =  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:
          lowercase__      ,   lowercase__           =  key.split('.*.'		)
          if prefix in name and suffix in name:
            lowercase__           =  suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
        if key in name:
          lowercase__           =  True
          if "*" in mapped_key:
            lowercase__           =  name.split(_SCREAMING_SNAKE_CASE		)[0].split('.'		)[-2]
            lowercase__           =  mapped_key.replace('*'		,						_SCREAMING_SNAKE_CASE		)
          if "weight_g" in name:
            lowercase__           =  'weight_g'
          elif "weight_v" in name:
            lowercase__           =  'weight_v'
          elif "bias" in name:
            lowercase__           =  'bias'
          elif "weight" in name:
            lowercase__           =  'weight'
          elif "running_mean" in name:
            lowercase__           =  'running_mean'
          elif "running_var" in name:
            lowercase__           =  'running_var'
          elif "num_batches_tracked" in name:
            lowercase__           =  'num_batches_tracked'
          else:
            lowercase__           =  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		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Tuple:
  lowercase__           =  full_name.split('conv_layers.'		)[-1]
  lowercase__           =  name.split('.'		)
  lowercase__           =  int(items[0]		)
  lowercase__           =  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."""		)
      lowercase__           =  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."""		)
      lowercase__           =  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."""		)
      lowercase__           =  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."""		)
      lowercase__           =  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		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE=None		,						_SCREAMING_SNAKE_CASE=None		,						_SCREAMING_SNAKE_CASE=None		,						)    ->       Optional[int]:
  if config_path is not None:
    lowercase__           =  SpeechTaConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
  else:
    lowercase__           =  SpeechTaConfig()

  if task == "s2t":
    lowercase__           =  config.max_text_positions
    lowercase__           =  SpeechTaForSpeechToText(_SCREAMING_SNAKE_CASE		)
  elif task == "t2s":
    lowercase__           =  1876
    lowercase__           =  600
    lowercase__           =  config.max_speech_positions
    lowercase__           =  SpeechTaForTextToSpeech(_SCREAMING_SNAKE_CASE		)
  elif task == "s2s":
    lowercase__           =  1876
    lowercase__           =  config.max_speech_positions
    lowercase__           =  SpeechTaForSpeechToSpeech(_SCREAMING_SNAKE_CASE		)
  else:
    raise ValueError(F"""Unknown task name: {task}"""		)

  if vocab_path:
    lowercase__           =  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
    lowercase__           =  AddedToken('<mask>'		,						lstrip=_SCREAMING_SNAKE_CASE		,						rstrip=_SCREAMING_SNAKE_CASE		)
    lowercase__           =  mask_token
    tokenizer.add_special_tokens({'mask_token': mask_token}		)
    tokenizer.add_tokens(['<ctc_blank>']		)

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

  lowercase__           =  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__":
      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,
      )









from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_          =		logging.get_logger(__name__)

lowercase_          =		{
    """transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		Optional[Any]			 =	'transfo-xl'
   _UpperCamelCase   :		Any			 =	['mems']
   _UpperCamelCase   :		Any			 =	{
       'n_token': 'vocab_size',
       'hidden_size': 'd_model',
       'num_attention_heads': 'n_head',
       'num_hidden_layers': 'n_layer',
   }


   def __init__(   self      :    Optional[Any]      ,					a      :    Optional[int]=267_735      ,					a      :    str=[20_000, 40_000, 200_000]      ,					a      :    str=1_024      ,					a      :    str=1_024      ,					a      :    int=16      ,					a      :    Optional[int]=64      ,					a      :    Optional[int]=4_096      ,					a      :    int=4      ,					a      :    Tuple=False      ,					a      :    Any=18      ,					a      :    Tuple=1_600      ,					a      :    Union[str, Any]=1_000      ,					a      :    str=True      ,					a      :    Dict=True      ,					a      :    Any=0      ,					a      :    List[Any]=-1      ,					a      :    List[Any]=True      ,					a      :    Tuple=0.1      ,					a      :    List[Any]=0.0      ,					a      :    Optional[Any]=True      ,					a      :    int="normal"      ,					a      :    Optional[Any]=0.01      ,					a      :    str=0.01      ,					a      :    List[Any]=0.02      ,					a      :    List[Any]=1E-5      ,					a      :    Optional[Any]=0      ,					**a      :    Optional[int]      ,					)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  vocab_size
     lowercase__           =  []
     self.cutoffs.extend(a	)
     if proj_share_all_but_first:
       lowercase__           =  [False] + [True] * len(self.cutoffs	)
     else:
       lowercase__           =  [False] + [False] * len(self.cutoffs	)
     lowercase__           =  d_model
     lowercase__           =  d_embed
     lowercase__           =  d_head
     lowercase__           =  d_inner
     lowercase__           =  div_val
     lowercase__           =  pre_lnorm
     lowercase__           =  n_layer
     lowercase__           =  n_head
     lowercase__           =  mem_len
     lowercase__           =  same_length
     lowercase__           =  attn_type
     lowercase__           =  clamp_len
     lowercase__           =  sample_softmax
     lowercase__           =  adaptive
     lowercase__           =  dropout
     lowercase__           =  dropatt
     lowercase__           =  untie_r
     lowercase__           =  init
     lowercase__           =  init_range
     lowercase__           =  proj_init_std
     lowercase__           =  init_std
     lowercase__           =  layer_norm_epsilon
     super().__init__(eos_token_id=a      ,					**a	)


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       Union[str, Any]:





     """simple docstring"""







     logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit."""	)
     return -1


   @max_position_embeddings.setter
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    Optional[int]	)->       Optional[int]:





     """simple docstring"""







     raise NotImplementedError(
         f"""The model {self.model_type} is one of the few models that has no sequence length limit."""	)


