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()
__lowerCamelCase			: int							   =		logging.get_logger('''transformers.models.speecht5''')

__lowerCamelCase			: Dict							   =		{
    '''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''',
}
__lowerCamelCase			: Optional[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''',
}
__lowerCamelCase			: int							   =		{
    '''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''',
}
__lowerCamelCase			: Optional[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''',
}
__lowerCamelCase			: Optional[int]							   =		{
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
__lowerCamelCase			: Optional[int]							   =		{
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
__lowerCamelCase			: Tuple							   =		{
    '''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''',
}
__lowerCamelCase			: Union[str, Any]							   =		{
    '''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''',
}
__lowerCamelCase			: int							   =		{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__lowerCamelCase			: int							   =		{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__lowerCamelCase			: Any							   =		{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__lowerCamelCase			: int							   =		[]
__lowerCamelCase			: 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''',
]
__lowerCamelCase			: Optional[int]							   =		IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
__lowerCamelCase			: List[str]							   =		IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
__lowerCamelCase			: List[str]							   =		IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       int ,     __UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       int ,     __UpperCamelCase    :       List[Any] ,     __UpperCamelCase    :       Optional[int]	)							->					List[str]:







  """simple docstring"""





  for attribute in key.split("""."""	):
    SCREAMING_SNAKE_CASE__										=      getattr(__UpperCamelCase ,     __UpperCamelCase	)

  if weight_type is not None:
    SCREAMING_SNAKE_CASE__										=      getattr(__UpperCamelCase ,     __UpperCamelCase	).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 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[Any] ,     __UpperCamelCase    :       int	)							->					Optional[Any]:







  """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 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       List[Any] ,     __UpperCamelCase    :       Tuple ,     __UpperCamelCase    :       List[Any]	)							->					int:







  """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(__UpperCamelCase ,     __UpperCamelCase	):
      logger.info(f"""{name} was ignored"""	)
      continue

    SCREAMING_SNAKE_CASE__										=      False
    if "conv_layers" in name:
      load_conv_layer(
          __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     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(__UpperCamelCase	)[0].split("""."""	)[-2]
            SCREAMING_SNAKE_CASE__										=      mapped_key.replace("""*""" ,     __UpperCamelCase	)
          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(__UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	)
        continue
    if not is_used:
      unused_weights.append(__UpperCamelCase	)

  logger.warning(f"""Unused weights: {unused_weights}"""	)



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       Any ,     __UpperCamelCase    :       List[str] ,     __UpperCamelCase    :       str ,     __UpperCamelCase    :       str	)							->					Union[str, Any]:







  """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(__UpperCamelCase	)



@torch.no_grad()
def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str ,     __UpperCamelCase    :       int ,     __UpperCamelCase    :       str ,     __UpperCamelCase    :       List[str]=None ,     __UpperCamelCase    :       str=None ,     __UpperCamelCase    :       List[str]=None ,     )							->					int:







  """simple docstring"""





  if config_path is not None:
    SCREAMING_SNAKE_CASE__										=      SpeechTaConfig.from_pretrained(__UpperCamelCase	)
  else:
    SCREAMING_SNAKE_CASE__										=      SpeechTaConfig()

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

  if vocab_path:
    SCREAMING_SNAKE_CASE__										=      SpeechTaTokenizer(__UpperCamelCase ,     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=__UpperCamelCase ,     rstrip=__UpperCamelCase	)
    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=__UpperCamelCase ,     feature_extractor=__UpperCamelCase	)
  processor.save_pretrained(__UpperCamelCase	)

  SCREAMING_SNAKE_CASE__										=      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__":
   __lowerCamelCase			: Optional[Any]							   =		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.'''
   )

   __lowerCamelCase			: List[Any]							   =		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,
   )

# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__lowerCamelCase			: Any							   =		abspath(join(dirname(dirname(__file__)), '''src'''))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int]	)							->					Any:







  """simple docstring"""





  from diffusers.utils.testing_utils import pytest_addoption_shared

  pytest_addoption_shared(__UpperCamelCase	)






def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Dict	)							->					Optional[Any]:







  """simple docstring"""





  from diffusers.utils.testing_utils import pytest_terminal_summary_main

  SCREAMING_SNAKE_CASE__										=      terminalreporter.config.getoption("""--make-reports"""	)
  if make_reports:
    pytest_terminal_summary_main(__UpperCamelCase ,     id=__UpperCamelCase	)
