




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

UpperCamelCase_    	=				{
    "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",
}
UpperCamelCase_    	=				{
    "text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
    "text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
UpperCamelCase_    	=				{
    "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",
}
UpperCamelCase_    	=				{
    "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",
}
UpperCamelCase_    	=				{
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
UpperCamelCase_    	=				{
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
UpperCamelCase_    	=				{
    "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",
}
UpperCamelCase_    	=				{
    "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",
}
UpperCamelCase_    	=				{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
UpperCamelCase_    	=				{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
UpperCamelCase_    	=				{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
UpperCamelCase_    	=				[]
UpperCamelCase_    	=				[
    "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",
]
UpperCamelCase_    	=				IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
UpperCamelCase_    	=				IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
UpperCamelCase_    	=				IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]




def   lowercase__(			__UpperCamelCase:					List[str]      ,__UpperCamelCase:					List[str]      ,__UpperCamelCase:					Tuple      ,__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					Tuple    ):

      """simple docstring"""






      for attribute in key.split('.'    ):
            SCREAMING_SNAKE_CASE   :      List[str]    					=						getattr(__UpperCamelCase      ,__UpperCamelCase    )

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

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




def   lowercase__(			__UpperCamelCase:					Any      ,__UpperCamelCase:					str    ):

      """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   :      Dict    					=						key.split('.*.'    )
                  if prefix in name and suffix in name:
                        return True
            elif key in name:
                  return True
      return False




def   lowercase__(			__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					Any    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[]

      if task == "s2t":
            SCREAMING_SNAKE_CASE   :      int    					=						hf_model.speechta.encoder.prenet.feature_encoder
            SCREAMING_SNAKE_CASE   :      str    					=						MAPPING_S2T
            SCREAMING_SNAKE_CASE   :      List[str]    					=						IGNORE_KEYS_S2T
      elif task == "t2s":
            SCREAMING_SNAKE_CASE   :      Dict    					=						None
            SCREAMING_SNAKE_CASE   :      Tuple    					=						MAPPING_T2S
            SCREAMING_SNAKE_CASE   :      str    					=						IGNORE_KEYS_T2S
      elif task == "s2s":
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						hf_model.speechta.encoder.prenet.feature_encoder
            SCREAMING_SNAKE_CASE   :      List[str]    					=						MAPPING_S2S
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						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   :      str    					=						False
            if "conv_layers" in name:
                  load_conv_layer(
                      __UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase      ,hf_model.config.feat_extract_norm == 'group'      ,)
                  SCREAMING_SNAKE_CASE   :      Optional[int]    					=						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   :      int    					=						key.split('.*.'    )
                              if prefix in name and suffix in name:
                                    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						suffix

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

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      str    					=						full_name.split('conv_layers.'    )[-1]
      SCREAMING_SNAKE_CASE   :      int    					=						name.split('.'    )
      SCREAMING_SNAKE_CASE   :      Any    					=						int(items[0]    )
      SCREAMING_SNAKE_CASE   :      Tuple    					=						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   :      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."    )
                  SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						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   :      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."    )
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						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   lowercase__(			__UpperCamelCase:					Optional[Any]      ,__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					int=None      ,__UpperCamelCase:					Optional[Any]=None      ,__UpperCamelCase:					int=None      ,):

      """simple docstring"""






      if config_path is not None:
            SCREAMING_SNAKE_CASE   :      Any    					=						SpeechTaConfig.from_pretrained(__UpperCamelCase    )
      else:
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						SpeechTaConfig()

      if task == "s2t":
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						config.max_text_positions
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						SpeechTaForSpeechToText(__UpperCamelCase    )
      elif task == "t2s":
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						18_76
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						6_00
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						config.max_speech_positions
            SCREAMING_SNAKE_CASE   :      List[str]    					=						SpeechTaForTextToSpeech(__UpperCamelCase    )
      elif task == "s2s":
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						18_76
            SCREAMING_SNAKE_CASE   :      int    					=						config.max_speech_positions
            SCREAMING_SNAKE_CASE   :      Any    					=						SpeechTaForSpeechToSpeech(__UpperCamelCase    )
      else:
            raise ValueError(f"Unknown task name: {task}"    )

      if vocab_path:
            SCREAMING_SNAKE_CASE   :      Any    					=						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   :      str    					=						AddedToken('<mask>'      ,lstrip=__UpperCamelCase      ,rstrip=__UpperCamelCase    )
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						mask_token
            tokenizer.add_special_tokens({'mask_token': mask_token}    )
            tokenizer.add_tokens(['<ctc_blank>']    )

      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						SpeechTaFeatureExtractor()
      SCREAMING_SNAKE_CASE   :      Dict    					=						SpeechTaProcessor(tokenizer=__UpperCamelCase      ,feature_extractor=__UpperCamelCase    )
      processor.save_pretrained(__UpperCamelCase    )

      SCREAMING_SNAKE_CASE   :      Any    					=						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__":
      UpperCamelCase_    	=				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."
      )

      UpperCamelCase_    	=				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   lowercase__(			__UpperCamelCase:					int = 1_00_00_00    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Tuple    					=						[i - 1 for i in range(limit + 1    )]

      for i in range(2      ,limit + 1    ):
            if phi[i] == i - 1:
                  for j in range(2 * i      ,limit + 1      ,__UpperCamelCase    ):
                        phi[j] -= phi[j] // i

      return sum(phi[2 : limit + 1]    )


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


