






import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__a:     Optional[int]   					=					logging.get_logger('''transformers.models.speecht5''')




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case )				->						Optional[Any]:
       hf_model.apply_weight_norm()

       _UpperCAmelCase							=    checkpoint["""input_conv.weight_g"""]
       _UpperCAmelCase							=    checkpoint["""input_conv.weight_v"""]
       _UpperCAmelCase							=    checkpoint["""input_conv.bias"""]

       for i in range(len(config.upsample_rates ) ):
              _UpperCAmelCase							=    checkpoint[f"""upsamples.{i}.1.weight_g"""]
              _UpperCAmelCase							=    checkpoint[f"""upsamples.{i}.1.weight_v"""]
              _UpperCAmelCase							=    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 ) ):
                     _UpperCAmelCase							=    checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_g"""]
                     _UpperCAmelCase							=    checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_v"""]
                     _UpperCAmelCase							=    checkpoint[f"""blocks.{i}.convs1.{j}.1.bias"""]

                     _UpperCAmelCase							=    checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_g"""]
                     _UpperCAmelCase							=    checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_v"""]
                     _UpperCAmelCase							=    checkpoint[f"""blocks.{i}.convs2.{j}.1.bias"""]

       _UpperCAmelCase							=    checkpoint["""output_conv.1.weight_g"""]
       _UpperCAmelCase							=    checkpoint["""output_conv.1.weight_v"""]
       _UpperCAmelCase							=    checkpoint["""output_conv.1.bias"""]

       hf_model.remove_weight_norm()




@torch.no_grad()
def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case=None   ,      __snake_case=None   ,      )				->						List[Any]:
       if config_path is not None:
              _UpperCAmelCase							=    SpeechTaHifiGanConfig.from_pretrained(__snake_case )
       else:
              _UpperCAmelCase							=    SpeechTaHifiGanConfig()

       _UpperCAmelCase							=    SpeechTaHifiGan(__snake_case )

       _UpperCAmelCase							=    torch.load(__snake_case )
       load_weights(orig_checkpoint["""model"""]["""generator"""]   ,      __snake_case   ,      __snake_case )

       _UpperCAmelCase							=    np.load(__snake_case )
       _UpperCAmelCase							=    stats[0].reshape(-1 )
       _UpperCAmelCase							=    stats[1].reshape(-1 )
       _UpperCAmelCase							=    torch.from_numpy(__snake_case ).float()
       _UpperCAmelCase							=    torch.from_numpy(__snake_case ).float()

       model.save_pretrained(__snake_case )

       if repo_id:
              print("""Pushing to the hub...""" )
              model.push_to_hub(__snake_case )


if __name__ == "__main__":
  __a:     List[str]   					=					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.'''
  )

  __a:     Dict   					=					parser.parse_args()
  convert_hifigan_checkpoint(
      args.checkpoint_path,
      args.stats_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.push_to_hub,
  )







import os
import sys

__a:     Union[str, Any]   					=					os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


__a:     Union[str, Any]   					=					[
    '''torch''',
    '''numpy''',
    '''tokenizers''',
    '''filelock''',
    '''requests''',
    '''tqdm''',
    '''regex''',
    '''sentencepiece''',
    '''sacremoses''',
    '''importlib_metadata''',
    '''huggingface_hub''',
]




@add_start_docstrings(AutoConfig.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Union[str, Any]:
       return AutoConfig.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoTokenizer.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Any:
       return AutoTokenizer.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModel.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Tuple:
       return AutoModel.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Tuple:
       return AutoModelForCausalLM.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Optional[Any]:
       return AutoModelForMaskedLM.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						List[str]:
       return AutoModelForSequenceClassification.from_pretrained(*__snake_case   ,      **__snake_case )





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						List[Any]:
       return AutoModelForQuestionAnswering.from_pretrained(*__snake_case   ,      **__snake_case )