






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

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







def      lowerCamelCase__       (							_lowerCamelCase       :		int		,				_lowerCamelCase       :		List[Any]		,				_lowerCamelCase       :		List[Any]		,				_lowerCamelCase       :		List[Any]		,				_lowerCamelCase       :		Optional[int] )      ->				str:
  for attribute in key.split('.' ):
    lowerCamelCase_         =						getattr(_lowerCamelCase		,				_lowerCamelCase )

  if weight_type is not None:
    lowerCamelCase_         =						getattr(_lowerCamelCase		,				_lowerCamelCase ).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      lowerCamelCase__       (							_lowerCamelCase       :		int		,				_lowerCamelCase       :		Optional[Any] )      ->				List[Any]:
  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      lowerCamelCase__       (							_lowerCamelCase       :		int		,				_lowerCamelCase       :		Tuple		,				_lowerCamelCase       :		Optional[Any] )      ->				Union[str, Any]:
  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(_lowerCamelCase		,				_lowerCamelCase ):
      logger.info(F'''{name} was ignored''' )
      continue

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

  logger.warning(F'''Unused weights: {unused_weights}''' )







def      lowerCamelCase__       (							_lowerCamelCase       :		str		,				_lowerCamelCase       :		Tuple		,				_lowerCamelCase       :		List[str]		,				_lowerCamelCase       :		Dict		,				_lowerCamelCase       :		Optional[int] )      ->				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(_lowerCamelCase )







@torch.no_grad()
def      lowerCamelCase__       (							_lowerCamelCase       :		Tuple		,				_lowerCamelCase       :		Optional[Any]		,				_lowerCamelCase       :		int		,				_lowerCamelCase       :		Optional[Any]=None		,				_lowerCamelCase       :		str=None		,				_lowerCamelCase       :		Optional[int]=None		,				)      ->				Union[str, Any]:
  if config_path is not None:
    lowerCamelCase_         =						SpeechTaConfig.from_pretrained(_lowerCamelCase )
  else:
    lowerCamelCase_         =						SpeechTaConfig()

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

  if vocab_path:
    lowerCamelCase_         =						SpeechTaTokenizer(_lowerCamelCase		,				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=_lowerCamelCase		,				rstrip=_lowerCamelCase )
    lowerCamelCase_         =						mask_token
    tokenizer.add_special_tokens({'mask_token': mask_token} )
    tokenizer.add_tokens(['<ctc_blank>'] )

  lowerCamelCase_         =						SpeechTaFeatureExtractor()
  lowerCamelCase_         =						SpeechTaProcessor(tokenizer=_lowerCamelCase		,				feature_extractor=_lowerCamelCase )
  processor.save_pretrained(_lowerCamelCase )

  lowerCamelCase_         =						torch.load(_lowerCamelCase )
  recursively_load_weights(fairseq_checkpoint['model']		,				_lowerCamelCase		,				_lowerCamelCase )

  model.save_pretrained(_lowerCamelCase )

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


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE					:				Union[str, 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.'''
  )

  _SCREAMING_SNAKE_CASE					:				Optional[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,
  )












"""simple docstring"""





import string







def      lowerCamelCase__       (							_lowerCamelCase       :		str )      ->				None:
  for key in range(len(string.ascii_uppercase ) ):
    lowerCamelCase_         =						''
    for symbol in message:
      if symbol in string.ascii_uppercase:
        lowerCamelCase_         =						string.ascii_uppercase.find(_lowerCamelCase )
        lowerCamelCase_         =						num - key
        if num < 0:
          lowerCamelCase_         =						num + len(string.ascii_uppercase )
        lowerCamelCase_         =						translated + string.ascii_uppercase[num]
      else:
        lowerCamelCase_         =						translated + symbol
    print(F'''Decryption using Key #{key}: {translated}''' )







def      lowerCamelCase__       (							)      ->				None:
  lowerCamelCase_         =						input('Encrypted message: ' )
  lowerCamelCase_         =						message.upper()
  decrypt(_lowerCamelCase )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




