
import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_lowerCamelCase			:							Union[str, Any]         =     logging.get_logger('''transformers.models.speecht5''')






def 			a_						(			__lowercase  :      Union[str, Any]			,   __lowercase  :      Optional[int]			,   __lowercase  :      Any					)					->    Any:
			hf_model.apply_weight_norm()

			_snake_case            =     checkpoint['input_conv.weight_g']
			_snake_case            =     checkpoint['input_conv.weight_v']
			_snake_case            =     checkpoint['input_conv.bias']

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

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

			_snake_case            =     checkpoint['output_conv.1.weight_g']
			_snake_case            =     checkpoint['output_conv.1.weight_v']
			_snake_case            =     checkpoint['output_conv.1.bias']

			hf_model.remove_weight_norm()






@torch.no_grad()
def 			a_						(			__lowercase  :      int			,   __lowercase  :      Dict			,   __lowercase  :      List[str]			,   __lowercase  :      List[Any]=None			,   __lowercase  :      str=None			,   )					->    Tuple:
			if config_path is not None:
						_snake_case            =     SpeechTaHifiGanConfig.from_pretrained(lowerCamelCase_					)
			else:
						_snake_case            =     SpeechTaHifiGanConfig()

			_snake_case            =     SpeechTaHifiGan(lowerCamelCase_					)

			_snake_case            =     torch.load(lowerCamelCase_					)
			load_weights(orig_checkpoint['model']['generator']			,   lowerCamelCase_			,   lowerCamelCase_					)

			_snake_case            =     np.load(lowerCamelCase_					)
			_snake_case            =     stats[0].reshape(-1					)
			_snake_case            =     stats[1].reshape(-1					)
			_snake_case            =     torch.from_numpy(lowerCamelCase_					).float()
			_snake_case            =     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			:							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.'''
			)

			_lowerCamelCase			:							Union[str, Any]         =     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     a     (		lowerCamelCase_							):






				'''simple docstring'''

				if p < 2:
								raise ValueError('''p should not be less than 2!'''							)
				elif p == 2:
								return True

				lowercase__  					=     4
				lowercase__  					=     (1 << p) - 1
				for _ in range(p - 2							):
								lowercase__  					=     ((s * s) - 2) % m
				return s == 0


if __name__ == "__main__":
							print(lucas_lehmer_test(7))
							print(lucas_lehmer_test(11))




