




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
a					  =     logging.get_logger('transformers.models.speecht5')


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
					hf_model.apply_weight_norm()

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

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

															lowercase_  							=			checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_g''']
															lowercase_  							=			checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_v''']
															lowercase_  							=			checkpoint[F'''blocks.{i}.convs2.{j}.1.bias''']

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

					hf_model.remove_weight_norm()


@torch.no_grad()
def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__=None						,							UpperCAmelCase__=None						,							):
					if config_path is not None:
										lowercase_  							=			SpeechTaHifiGanConfig.from_pretrained(UpperCAmelCase__  )
					else:
										lowercase_  							=			SpeechTaHifiGanConfig()

					lowercase_  							=			SpeechTaHifiGan(UpperCAmelCase__  )

					lowercase_  							=			torch.load(UpperCAmelCase__  )
					load_weights(orig_checkpoint["""model"""]["""generator"""]						,							UpperCAmelCase__						,							UpperCAmelCase__  )

					lowercase_  							=			np.load(UpperCAmelCase__  )
					lowercase_  							=			stats[0].reshape(-1  )
					lowercase_  							=			stats[1].reshape(-1  )
					lowercase_  							=			torch.from_numpy(UpperCAmelCase__  ).float()
					lowercase_  							=			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__":
							a					  =     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					  =     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 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					return " ".join(input_str.split()[::-1]  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()


