

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      a      (			A__	:   int      ,   A__	:   List[Any]      ,   A__	:   List[Any]      ,   A__	:   List[Any]      ,   A__	:   Optional[int]     )    ->      str:





      """simple docstring"""



      for attribute in key.split('.'     ):
            _lowercase	       =getattr(_lowerCamelCase      ,   _lowerCamelCase     )

      if weight_type is not None:
            _lowercase	       =getattr(_lowerCamelCase      ,   _lowerCamelCase     ).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      a      (			A__	:   int      ,   A__	:   Optional[Any]     )    ->      List[Any]:





      """simple docstring"""



      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      a      (			A__	:   int      ,   A__	:   Tuple      ,   A__	:   Optional[Any]     )    ->      Union[str, Any]:





      """simple docstring"""



      _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(_lowerCamelCase      ,   _lowerCamelCase     ):
                  logger.info(F'''{name} was ignored'''     )
                  continue

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

      logger.warning(F'''Unused weights: {unused_weights}'''     )
def      a      (			A__	:   str      ,   A__	:   Tuple      ,   A__	:   List[str]      ,   A__	:   Dict      ,   A__	:   Optional[int]     )    ->      str:





      """simple docstring"""



      _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(_lowerCamelCase     )
@torch.no_grad()
def      a      (			A__	:   Tuple      ,   A__	:   Optional[Any]      ,   A__	:   int      ,   A__	:   Optional[Any]=None      ,   A__	:   str=None      ,   A__	:   Optional[int]=None      ,   )    ->      Union[str, Any]:





      """simple docstring"""



      if config_path is not None:
            _lowercase	       =SpeechTaConfig.from_pretrained(_lowerCamelCase     )
      else:
            _lowercase	       =SpeechTaConfig()

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

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

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

      _lowercase	       =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__":
  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"""





from __future__ import annotations







def      lowerCamelCase__       (							_lowerCamelCase       :		int )      ->				list[int]:
  lowerCamelCase_         =						[True] * limit
  lowerCamelCase_         =						False
  lowerCamelCase_         =						False
  lowerCamelCase_         =						True

  for i in range(3		,				int(limit**0.5 + 1 )		,				2 ):
    lowerCamelCase_         =						i * 2
    while index < limit:
      lowerCamelCase_         =						False
      lowerCamelCase_         =						index + i

  lowerCamelCase_         =						[2]

  for i in range(3		,				_lowerCamelCase		,				2 ):
    if is_prime[i]:
      primes.append(_lowerCamelCase )

  return primes







def      lowerCamelCase__       (							_lowerCamelCase       :		int = 1000000 )      ->				int:
  lowerCamelCase_         =						prime_sieve(_lowerCamelCase )
  lowerCamelCase_         =						0
  lowerCamelCase_         =						0

  for i in range(len(_lowerCamelCase ) ):
    for j in range(i + length		,				len(_lowerCamelCase ) ):
      lowerCamelCase_         =						sum(primes[i:j] )
      if sol >= ceiling:
        break

      if sol in primes:
        lowerCamelCase_         =						j - i
        lowerCamelCase_         =						sol

  return largest


if __name__ == "__main__":
  print(F'''{solution() = }''')




