






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     :							List[str]    =logging.get_logger('''transformers.models.speecht5''')

lowerCamelCase     :							int    ={
    '''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     :							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''',
}
lowerCamelCase     :							Union[str, Any]    ={
    '''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     :							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[Any]    ={
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
lowerCamelCase     :							int    ={
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
lowerCamelCase     :							Dict    ={
    '''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     :							Optional[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     :							Union[str, Any]    ={
    **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     :							Tuple    ={
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCamelCase     :							List[Any]    =[]
lowerCamelCase     :							List[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     :							Dict    =IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
lowerCamelCase     :							List[Any]    =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							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     List[Any]:
     for attribute in key.split("."      ):
          UpperCamelCase__      :       List[str]	    =  getattr(__lowerCAmelCase ,   __lowerCAmelCase      )

     if weight_type is not None:
          UpperCamelCase__      :       Dict	    =  getattr(__lowerCAmelCase ,   __lowerCAmelCase      ).shape
     else:
          UpperCamelCase__      :       Optional[Any]	    =  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":
          UpperCamelCase__      :       int	    =  value
     elif weight_type == "weight_g":
          UpperCamelCase__      :       Optional[Any]	    =  value
     elif weight_type == "weight_v":
          UpperCamelCase__      :       List[Any]	    =  value
     elif weight_type == "bias":
          UpperCamelCase__      :       List[Any]	    =  value
     elif weight_type == "running_mean":
          UpperCamelCase__      :       int	    =  value
     elif weight_type == "running_var":
          UpperCamelCase__      :       Dict	    =  value
     elif weight_type == "num_batches_tracked":
          UpperCamelCase__      :       Optional[int]	    =  value
     else:
          UpperCamelCase__      :       str	    =  value

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







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







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     str:
     UpperCamelCase__      :       Any	    =  []

     if task == "s2t":
          UpperCamelCase__      :       List[Any]	    =  hf_model.speechta.encoder.prenet.feature_encoder
          UpperCamelCase__      :       Dict	    =  MAPPING_S2T
          UpperCamelCase__      :       Optional[int]	    =  IGNORE_KEYS_S2T
     elif task == "t2s":
          UpperCamelCase__      :       List[Any]	    =  None
          UpperCamelCase__      :       int	    =  MAPPING_T2S
          UpperCamelCase__      :       str	    =  IGNORE_KEYS_T2S
     elif task == "s2s":
          UpperCamelCase__      :       str	    =  hf_model.speechta.encoder.prenet.feature_encoder
          UpperCamelCase__      :       Tuple	    =  MAPPING_S2S
          UpperCamelCase__      :       int	    =  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

          UpperCamelCase__      :       Optional[Any]	    =  False
          if "conv_layers" in name:
               load_conv_layer(
                   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   hf_model.config.feat_extract_norm == "group" ,   )
               UpperCamelCase__      :       Union[str, Any]	    =  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:
                         UpperCamelCase__    ,					UpperCamelCase__      :       Tuple	    =  key.split(".*."      )
                         if prefix in name and suffix in name:
                              UpperCamelCase__      :       Any	    =  suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                    if key in name:
                         UpperCamelCase__      :       Any	    =  True
                         if "*" in mapped_key:
                              UpperCamelCase__      :       List[Any]	    =  name.split(__lowerCAmelCase      )[0].split("."      )[-2]
                              UpperCamelCase__      :       Dict	    =  mapped_key.replace("*" ,   __lowerCAmelCase      )
                         if "weight_g" in name:
                              UpperCamelCase__      :       Union[str, Any]	    =  "weight_g"
                         elif "weight_v" in name:
                              UpperCamelCase__      :       Union[str, Any]	    =  "weight_v"
                         elif "bias" in name:
                              UpperCamelCase__      :       Dict	    =  "bias"
                         elif "weight" in name:
                              UpperCamelCase__      :       int	    =  "weight"
                         elif "running_mean" in name:
                              UpperCamelCase__      :       Dict	    =  "running_mean"
                         elif "running_var" in name:
                              UpperCamelCase__      :       List[str]	    =  "running_var"
                         elif "num_batches_tracked" in name:
                              UpperCamelCase__      :       Optional[int]	    =  "num_batches_tracked"
                         else:
                              UpperCamelCase__      :       Any	    =  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 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     Optional[int]:
     UpperCamelCase__      :       Dict	    =  full_name.split("conv_layers."      )[-1]
     UpperCamelCase__      :       List[str]	    =  name.split("."      )
     UpperCamelCase__      :       Any	    =  int(items[0]      )
     UpperCamelCase__      :       Any	    =  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.'      )
               UpperCamelCase__      :       List[str]	    =  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.'      )
               UpperCamelCase__      :       Tuple	    =  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.'      )
               UpperCamelCase__      :       Union[str, Any]	    =  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.'      )
               UpperCamelCase__      :       Dict	    =  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 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase=None ,   __lowerCAmelCase=None ,   __lowerCAmelCase=None ,   )			->     Union[str, Any]:
     if config_path is not None:
          UpperCamelCase__      :       Optional[int]	    =  SpeechTaConfig.from_pretrained(__lowerCAmelCase      )
     else:
          UpperCamelCase__      :       Tuple	    =  SpeechTaConfig()

     if task == "s2t":
          UpperCamelCase__      :       Any	    =  config.max_text_positions
          UpperCamelCase__      :       str	    =  SpeechTaForSpeechToText(__lowerCAmelCase      )
     elif task == "t2s":
          UpperCamelCase__      :       Dict	    =  1876
          UpperCamelCase__      :       List[str]	    =  600
          UpperCamelCase__      :       Optional[int]	    =  config.max_speech_positions
          UpperCamelCase__      :       Optional[Any]	    =  SpeechTaForTextToSpeech(__lowerCAmelCase      )
     elif task == "s2s":
          UpperCamelCase__      :       Any	    =  1876
          UpperCamelCase__      :       Tuple	    =  config.max_speech_positions
          UpperCamelCase__      :       List[str]	    =  SpeechTaForSpeechToSpeech(__lowerCAmelCase      )
     else:
          raise ValueError(f'Unknown task name: {task}'      )

     if vocab_path:
          UpperCamelCase__      :       Optional[int]	    =  SpeechTaTokenizer(__lowerCAmelCase ,   model_max_length=config.max_text_positions      )

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

     UpperCamelCase__      :       List[Any]	    =  SpeechTaFeatureExtractor()
     UpperCamelCase__      :       Optional[Any]	    =  SpeechTaProcessor(tokenizer=__lowerCAmelCase ,   feature_extractor=__lowerCAmelCase      )
     processor.save_pretrained(__lowerCAmelCase      )

     UpperCamelCase__      :       int	    =  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__":
 lowerCamelCase     :							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.'''
 )

 lowerCamelCase     :							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,
 )







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase     :							List[Any]    =logging.get_logger(__name__)

lowerCamelCase     :							Optional[Any]    ={
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}





class       __a  (   A__	,       A__	):
 _lowerCAmelCase	:	Union[str, Any]				=			'''bit'''
 _lowerCAmelCase	:	List[str]				=			['''preactivation''', '''bottleneck''']
 _lowerCAmelCase	:	Any				=			['''SAME''', '''VALID''']







 def __init__(		self :       str							,       SCREAMING_SNAKE_CASE :       Tuple=3							,       SCREAMING_SNAKE_CASE :       str=64							,       SCREAMING_SNAKE_CASE :       List[Any]=[2_56, 5_12, 10_24, 20_48]							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=[3, 4, 6, 3]							,       SCREAMING_SNAKE_CASE :       str="preactivation"							,       SCREAMING_SNAKE_CASE :       Any="relu"							,       SCREAMING_SNAKE_CASE :       Dict=None							,       SCREAMING_SNAKE_CASE :       List[Any]=32							,       SCREAMING_SNAKE_CASE :       Tuple=0.0							,       SCREAMING_SNAKE_CASE :       Dict=False							,       SCREAMING_SNAKE_CASE :       int=32							,       SCREAMING_SNAKE_CASE :       str=1							,       SCREAMING_SNAKE_CASE :       List[str]=None							,       SCREAMING_SNAKE_CASE :       Optional[int]=None							,       **SCREAMING_SNAKE_CASE :       int							,       ):


      '''simple docstring'''






      super().__init__(**SCREAMING_SNAKE_CASE						)
      if layer_type not in self.layer_types:
           raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types						)}'						)
      if global_padding is not None:
           if global_padding.upper() in self.supported_padding:
                UpperCamelCase__      :       Any	    =  global_padding.upper()
           else:
                raise ValueError(F'Padding strategy {global_padding} not supported'						)
      UpperCamelCase__      :       Dict	    =  num_channels
      UpperCamelCase__      :       Dict	    =  embedding_size
      UpperCamelCase__      :       Tuple	    =  hidden_sizes
      UpperCamelCase__      :       Any	    =  depths
      UpperCamelCase__      :       Optional[int]	    =  layer_type
      UpperCamelCase__      :       int	    =  hidden_act
      UpperCamelCase__      :       str	    =  global_padding
      UpperCamelCase__      :       Any	    =  num_groups
      UpperCamelCase__      :       str	    =  drop_path_rate
      UpperCamelCase__      :       Optional[Any]	    =  embedding_dynamic_padding
      UpperCamelCase__      :       Tuple	    =  output_stride
      UpperCamelCase__      :       List[str]	    =  width_factor

      UpperCamelCase__      :       Any	    =  ["stem"] + [F'stage{idx}' for idx in range(1							,       len(SCREAMING_SNAKE_CASE						) + 1						)]
      UpperCamelCase__    ,					UpperCamelCase__      :       Union[str, Any]	    =  get_aligned_output_features_output_indices(
          out_features=SCREAMING_SNAKE_CASE							,       out_indices=SCREAMING_SNAKE_CASE							,       stage_names=self.stage_names						)