




'''simple docstring'''



UpperCamelCase_    	=				[
    "Audio",
    "Array2D",
    "Array3D",
    "Array4D",
    "Array5D",
    "ClassLabel",
    "Features",
    "Sequence",
    "Value",
    "Image",
    "Translation",
    "TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages








'''simple docstring'''



import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


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




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

      """simple docstring"""






      hf_model.apply_weight_norm()

      SCREAMING_SNAKE_CASE   :      Any    					=						checkpoint['input_conv.weight_g']
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						checkpoint['input_conv.weight_v']
      SCREAMING_SNAKE_CASE   :      str    					=						checkpoint['input_conv.bias']

      for i in range(len(config.upsample_rates    )    ):
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						checkpoint[f"upsamples.{i}.1.weight_g"]
            SCREAMING_SNAKE_CASE   :      Dict    					=						checkpoint[f"upsamples.{i}.1.weight_v"]
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						checkpoint[f"upsamples.{i}.1.bias"]

      for i in range(len(config.upsample_rates    ) * len(config.resblock_kernel_sizes    )    ):
            for j in range(len(config.resblock_dilation_sizes    )    ):
                  SCREAMING_SNAKE_CASE   :      int    					=						checkpoint[f"blocks.{i}.convs1.{j}.1.weight_g"]
                  SCREAMING_SNAKE_CASE   :      str    					=						checkpoint[f"blocks.{i}.convs1.{j}.1.weight_v"]
                  SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						checkpoint[f"blocks.{i}.convs1.{j}.1.bias"]

                  SCREAMING_SNAKE_CASE   :      Dict    					=						checkpoint[f"blocks.{i}.convs2.{j}.1.weight_g"]
                  SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						checkpoint[f"blocks.{i}.convs2.{j}.1.weight_v"]
                  SCREAMING_SNAKE_CASE   :      Tuple    					=						checkpoint[f"blocks.{i}.convs2.{j}.1.bias"]

      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						checkpoint['output_conv.1.weight_g']
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						checkpoint['output_conv.1.weight_v']
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						checkpoint['output_conv.1.bias']

      hf_model.remove_weight_norm()




@torch.no_grad()
def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					int      ,__UpperCamelCase:					Any      ,__UpperCamelCase:					str=None      ,__UpperCamelCase:					Tuple=None      ,):

      """simple docstring"""






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

      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						SpeechTaHifiGan(__UpperCamelCase    )

      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						torch.load(__UpperCamelCase    )
      load_weights(orig_checkpoint['model']['generator']      ,__UpperCamelCase      ,__UpperCamelCase    )

      SCREAMING_SNAKE_CASE   :      int    					=						np.load(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						stats[0].reshape(-1    )
      SCREAMING_SNAKE_CASE   :      Tuple    					=						stats[1].reshape(-1    )
      SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.from_numpy(__UpperCamelCase    ).float()
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						torch.from_numpy(__UpperCamelCase    ).float()

      model.save_pretrained(__UpperCamelCase    )

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


if __name__ == "__main__":
      UpperCamelCase_    	=				argparse.ArgumentParser()
      parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
      parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
      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_hifigan_checkpoint(
          args.checkpoint_path,
          args.stats_path,
          args.pytorch_dump_folder_path,
          args.config_path,
          args.push_to_hub,
      )


