




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

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



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Tuple:


      '''simple docstring'''


      for attribute in key.split("""."""     ):
            SCREAMING_SNAKE_CASE		=						getattr(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )

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

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



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       List[str]:


      '''simple docstring'''


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



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Any:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						[]

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

            SCREAMING_SNAKE_CASE		=						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"""			,						)
                  SCREAMING_SNAKE_CASE		=						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:
                              SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						key.split(""".*."""     )
                              if prefix in name and suffix in name:
                                    SCREAMING_SNAKE_CASE		=						suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                        if key in name:
                              SCREAMING_SNAKE_CASE		=						True
                              if "*" in mapped_key:
                                    SCREAMING_SNAKE_CASE		=						name.split(_SCREAMING_SNAKE_CASE     )[0].split("""."""     )[-2]
                                    SCREAMING_SNAKE_CASE		=						mapped_key.replace("""*"""			,						_SCREAMING_SNAKE_CASE     )
                              if "weight_g" in name:
                                    SCREAMING_SNAKE_CASE		=						"""weight_g"""
                              elif "weight_v" in name:
                                    SCREAMING_SNAKE_CASE		=						"""weight_v"""
                              elif "bias" in name:
                                    SCREAMING_SNAKE_CASE		=						"""bias"""
                              elif "weight" in name:
                                    SCREAMING_SNAKE_CASE		=						"""weight"""
                              elif "running_mean" in name:
                                    SCREAMING_SNAKE_CASE		=						"""running_mean"""
                              elif "running_var" in name:
                                    SCREAMING_SNAKE_CASE		=						"""running_var"""
                              elif "num_batches_tracked" in name:
                                    SCREAMING_SNAKE_CASE		=						"""num_batches_tracked"""
                              else:
                                    SCREAMING_SNAKE_CASE		=						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        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       str:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						full_name.split("""conv_layers."""     )[-1]
      SCREAMING_SNAKE_CASE		=						name.split("""."""     )
      SCREAMING_SNAKE_CASE		=						int(items[0]     )
      SCREAMING_SNAKE_CASE		=						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."""     )
                  SCREAMING_SNAKE_CASE		=						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."""     )
                  SCREAMING_SNAKE_CASE		=						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."""     )
                  SCREAMING_SNAKE_CASE		=						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."""     )
                  SCREAMING_SNAKE_CASE		=						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        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=None			,						_SCREAMING_SNAKE_CASE=None			,						_SCREAMING_SNAKE_CASE=None			,						)			->       int:


      '''simple docstring'''


      if config_path is not None:
            SCREAMING_SNAKE_CASE		=						SpeechTaConfig.from_pretrained(_SCREAMING_SNAKE_CASE     )
      else:
            SCREAMING_SNAKE_CASE		=						SpeechTaConfig()

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

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

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

      SCREAMING_SNAKE_CASE		=						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__":
      SCREAMING_SNAKE_CASE_						       =	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."""
      )

      SCREAMING_SNAKE_CASE_						       =	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 __future__ import annotations



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       list[int]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						2
      SCREAMING_SNAKE_CASE		=						[]
      while i * i <= n:
            if n % i:
                  i += 1
            else:
                  n //= i
                  factors.append(_SCREAMING_SNAKE_CASE     )
      if n > 1:
            factors.append(_SCREAMING_SNAKE_CASE     )
      return factors


if __name__ == "__main__":
      import doctest

      doctest.testmod()


