


"""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()
__snake_case		=       logging.get_logger('''transformers.models.speecht5''')

__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""",
}
__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""",
}
__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""",
}
__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""",
}
__snake_case		=       {
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
__snake_case		=       {
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
__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""",
}
__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""",
}
__snake_case		=       {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__snake_case		=       {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__snake_case		=       {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__snake_case		=       []
__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""",
]
__snake_case		=       IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
__snake_case		=       IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
__snake_case		=       IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
def       A_   (   _lowerCAmelCase     :					Tuple,     _lowerCAmelCase     :					Optional[Any],     _lowerCAmelCase     :					List[Any],     _lowerCAmelCase     :					int,     _lowerCAmelCase     :					str       ):






     """simple docstring"""

     for attribute in key.split('''.'''       ):
          _a    	=				getattr(_lowerCAmelCase,     _lowerCAmelCase       )

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

     logger.info(f'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'       )
def       A_   (   _lowerCAmelCase     :					int,     _lowerCAmelCase     :					List[str]       ):






     """simple docstring"""

     for key in ignore_keys:
          if key.endswith('''.*'''       ):
               if name.startswith(key[:-1]       ):
                    return True
          elif ".*." in key:
               _a	,      _a    	=				key.split('''.*.'''       )
               if prefix in name and suffix in name:
                    return True
          elif key in name:
               return True
     return False
def       A_   (   _lowerCAmelCase     :					Optional[Any],     _lowerCAmelCase     :					Any,     _lowerCAmelCase     :					Union[str, Any]       ):






     """simple docstring"""

     _a    	=				[]

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

          _a    	=				False
          if "conv_layers" in name:
               load_conv_layer(
                   _lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase,     hf_model.config.feat_extract_norm == '''group''',     )
               _a    	=				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:
                         _a	,      _a    	=				key.split('''.*.'''       )
                         if prefix in name and suffix in name:
                              _a    	=				suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                    if key in name:
                         _a    	=				True
                         if "*" in mapped_key:
                              _a    	=				name.split(_lowerCAmelCase       )[0].split('''.'''       )[-2]
                              _a    	=				mapped_key.replace('''*''',     _lowerCAmelCase       )
                         if "weight_g" in name:
                              _a    	=				'''weight_g'''
                         elif "weight_v" in name:
                              _a    	=				'''weight_v'''
                         elif "bias" in name:
                              _a    	=				'''bias'''
                         elif "weight" in name:
                              _a    	=				'''weight'''
                         elif "running_mean" in name:
                              _a    	=				'''running_mean'''
                         elif "running_var" in name:
                              _a    	=				'''running_var'''
                         elif "num_batches_tracked" in name:
                              _a    	=				'''num_batches_tracked'''
                         else:
                              _a    	=				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_   (   _lowerCAmelCase     :					Any,     _lowerCAmelCase     :					List[Any],     _lowerCAmelCase     :					Optional[Any],     _lowerCAmelCase     :					Tuple,     _lowerCAmelCase     :					Optional[int]       ):






     """simple docstring"""

     _a    	=				full_name.split('''conv_layers.'''       )[-1]
     _a    	=				name.split('''.'''       )
     _a    	=				int(items[0]       )
     _a    	=				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.'       )
               _a    	=				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.'       )
               _a    	=				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.'       )
               _a    	=				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.'       )
               _a    	=				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_   (   _lowerCAmelCase     :					int,     _lowerCAmelCase     :					Tuple,     _lowerCAmelCase     :					List[Any],     _lowerCAmelCase     :					List[Any]=None,     _lowerCAmelCase     :					Tuple=None,     _lowerCAmelCase     :					str=None,     ):






     """simple docstring"""

     if config_path is not None:
          _a    	=				SpeechTaConfig.from_pretrained(_lowerCAmelCase       )
     else:
          _a    	=				SpeechTaConfig()

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

     if vocab_path:
          _a    	=				SpeechTaTokenizer(_lowerCAmelCase,     model_max_length=config.max_text_positions       )

          # Mask token behaves like a normal word, i.e. include the space before it
          _a    	=				AddedToken('''<mask>''',     lstrip=_lowerCAmelCase,     rstrip=_lowerCAmelCase       )
          _a    	=				mask_token
          tokenizer.add_special_tokens({'''mask_token''': mask_token}       )
          tokenizer.add_tokens(['''<ctc_blank>''']       )

     _a    	=				SpeechTaFeatureExtractor()
     _a    	=				SpeechTaProcessor(tokenizer=_lowerCAmelCase,     feature_extractor=_lowerCAmelCase       )
     processor.save_pretrained(_lowerCAmelCase       )

     _a    	=				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__":
   __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.'''
   )

   __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,
   )



"""simple docstring"""






def       A_   (   _lowerCAmelCase     :					Dict=2_81_23       ):






     """simple docstring"""

     _a    	=				[1] * (limit + 1)

     for i in range(2,     int(limit**0.5       ) + 1       ):
          sum_divs[i * i] += i
          for k in range(i + 1,     limit // i + 1       ):
               sum_divs[k * i] += k + i

     _a    	=				set()
     _a    	=				0

     for n in range(1,     limit + 1       ):
          if sum_divs[n] > n:
               abundants.add(_lowerCAmelCase       )

          if not any((n - a in abundants) for a in abundants       ):
               res += n

     return res


if __name__ == "__main__":
   print(solution())