
import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


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


def 			UpperCamelCase					(    __lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : str				,					__lowerCamelCase     : List[str]    ):
       hf_model.apply_weight_norm()

       snake_case       :   Optional[Any]							=      checkpoint["input_conv.weight_g"]
       snake_case       :   Union[str, Any]							=      checkpoint["input_conv.weight_v"]
       snake_case       :   List[Any]							=      checkpoint["input_conv.bias"]

       for i in range(len(config.upsample_rates    )    ):
              snake_case       :   List[Any]							=      checkpoint[f"""upsamples.{i}.1.weight_g"""]
              snake_case       :   Dict							=      checkpoint[f"""upsamples.{i}.1.weight_v"""]
              snake_case       :   Dict							=      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    )    ):
                     snake_case       :   Union[str, Any]							=      checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_g"""]
                     snake_case       :   List[str]							=      checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_v"""]
                     snake_case       :   Any							=      checkpoint[f"""blocks.{i}.convs1.{j}.1.bias"""]

                     snake_case       :   Any							=      checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_g"""]
                     snake_case       :   Optional[int]							=      checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_v"""]
                     snake_case       :   List[Any]							=      checkpoint[f"""blocks.{i}.convs2.{j}.1.bias"""]

       snake_case       :   List[str]							=      checkpoint["output_conv.1.weight_g"]
       snake_case       :   Optional[int]							=      checkpoint["output_conv.1.weight_v"]
       snake_case       :   int							=      checkpoint["output_conv.1.bias"]

       hf_model.remove_weight_norm()


@torch.no_grad()
def 			UpperCamelCase					(    __lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : Optional[Any]				,					__lowerCamelCase     : Dict				,					__lowerCamelCase     : Optional[int]=None				,					__lowerCamelCase     : Dict=None				,					):
       if config_path is not None:
              snake_case       :   Any							=      SpeechTaHifiGanConfig.from_pretrained(__lowerCamelCase    )
       else:
              snake_case       :   List[Any]							=      SpeechTaHifiGanConfig()

       snake_case       :   Tuple							=      SpeechTaHifiGan(__lowerCamelCase    )

       snake_case       :   Any							=      torch.load(__lowerCamelCase    )
       load_weights(orig_checkpoint["model"]["generator"]				,					__lowerCamelCase				,					__lowerCamelCase    )

       snake_case       :   int							=      np.load(__lowerCamelCase    )
       snake_case       :   List[str]							=      stats[0].reshape(-1    )
       snake_case       :   Dict							=      stats[1].reshape(-1    )
       snake_case       :   Optional[Any]							=      torch.from_numpy(__lowerCamelCase    ).float()
       snake_case       :   Union[str, Any]							=      torch.from_numpy(__lowerCamelCase    ).float()

       model.save_pretrained(__lowerCamelCase    )

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


if __name__ == "__main__":
   __lowerCamelCase							=       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."""
   )

   __lowerCamelCase							=       parser.parse_args()
   convert_hifigan_checkpoint(
       args.checkpoint_path,
       args.stats_path,
       args.pytorch_dump_folder_path,
       args.config_path,
       args.push_to_hub,
   )












def  _SCREAMING_SNAKE_CASE				(   a					,						a = 0  )      ->      list:
					__A	:						int       =				length or len(a  )
					__A	:						str       =				False
					for i in range(length - 1  ):
										if list_data[i] > list_data[i + 1]:
															__A    ,				__A	:						Optional[int]       =				list_data[i + 1], list_data[i]
															__A	:						Union[str, Any]       =				True

					return list_data if not swapped else bubble_sort(a					,						length - 1  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()




