






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  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__   ) ->      int:
						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__    (    lowercase__						,       lowercase__						,       lowercase__						,       lowercase__=None						,       lowercase__=None						,       ) ->      int:
						if config_path is not None:
												__lowercase					=				SpeechTaHifiGanConfig.from_pretrained(SCREAMING_SNAKE_CASE_   )
						else:
												__lowercase					=				SpeechTaHifiGanConfig()

						__lowercase					=				SpeechTaHifiGan(SCREAMING_SNAKE_CASE_   )

						__lowercase					=				torch.load(SCREAMING_SNAKE_CASE_   )
						load_weights(orig_checkpoint["""model"""]["""generator"""]						,       SCREAMING_SNAKE_CASE_						,       SCREAMING_SNAKE_CASE_   )

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

						model.save_pretrained(SCREAMING_SNAKE_CASE_   )

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


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,
			)










UpperCamelCase__ =						{
    "joule": 1.0,
    "kilojoule": 10_00,
    "megajoule": 1_00_00_00,
    "gigajoule": 10_00_00_00_00,
    "wattsecond": 1.0,
    "watthour": 36_00,
    "kilowatthour": 3_60_00_00,
    "newtonmeter": 1.0,
    "calorie_nutr": 41_86.8,
    "kilocalorie_nutr": 4_18_68_00.00,
    "electronvolt": 1.6_0217_6634e-19,
    "britishthermalunit_it": 10_55.0_55_85,
    "footpound": 1.355_818,
}






def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__   ) ->      float:
						if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
												__lowercase					=				(
												    F"Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
												    F"Valid values are: {', '.join(lowercase__   )}"
												)
												raise ValueError(lowercase__   )
						return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
			import doctest

			doctest.testmod()





