





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


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







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      int:
					hf_model.apply_weight_norm()

					__A	:						List[str]       =				checkpoint['input_conv.weight_g']
					__A	:						List[Any]       =				checkpoint['input_conv.weight_v']
					__A	:						str       =				checkpoint['input_conv.bias']

					for i in range(len(config.upsample_rates  )  ):
										__A	:						Tuple       =				checkpoint[F"""upsamples.{i}.1.weight_g"""]
										__A	:						str       =				checkpoint[F"""upsamples.{i}.1.weight_v"""]
										__A	:						int       =				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  )  ):
															__A	:						Optional[Any]       =				checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
															__A	:						List[Any]       =				checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
															__A	:						Optional[Any]       =				checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

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

					__A	:						int       =				checkpoint['output_conv.1.weight_g']
					__A	:						Optional[Any]       =				checkpoint['output_conv.1.weight_v']
					__A	:						Dict       =				checkpoint['output_conv.1.bias']

					hf_model.remove_weight_norm()







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

					__A	:						List[Any]       =				SpeechTaHifiGan(a  )

					__A	:						List[str]       =				torch.load(a  )
					load_weights(orig_checkpoint['model']['generator']					,						a					,						a  )

					__A	:						str       =				np.load(a  )
					__A	:						int       =				stats[0].reshape(-1  )
					__A	:						Any       =				stats[1].reshape(-1  )
					__A	:						Optional[int]       =				torch.from_numpy(a  ).float()
					__A	:						Any       =				torch.from_numpy(a  ).float()

					model.save_pretrained(a  )

					if repo_id:
										print('Pushing to the hub...'  )
										model.push_to_hub(a  )


if __name__ == "__main__":
				UpperCAmelCase						:   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.'''
				)

				UpperCAmelCase						:   int			  =     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  )      ->      Tuple:
					__A    ,				__A	:						Optional[Any]       =				[], []
					while len(a  ) > 1:
										__A    ,				__A	:						Any       =				min(a  ), max(a  )
										start.append(a  )
										end.append(a  )
										collection.remove(a  )
										collection.remove(a  )
					end.reverse()
					return start + collection + end


if __name__ == "__main__":
				UpperCAmelCase						:   int			  =     input('''Enter numbers separated by a comma:\n''').strip()
				UpperCAmelCase						:   Dict			  =     [int(item) for item in user_input.split(''',''')]
				print(*merge_sort(unsorted), sep=''',''')




