

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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					hf_model.apply_weight_norm()

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

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

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

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

					hf_model.remove_weight_norm()


@torch.no_grad()
def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__=None		,					UpperCAmelCase__=None		,					):

					"""simple docstring"""






					if config_path is not None:
										__lowerCAmelCase					=							SpeechTaHifiGanConfig.from_pretrained(UpperCAmelCase__      )
					else:
										__lowerCAmelCase					=							SpeechTaHifiGanConfig()

					__lowerCAmelCase					=							SpeechTaHifiGan(UpperCAmelCase__      )

					__lowerCAmelCase					=							torch.load(UpperCAmelCase__      )
					load_weights(orig_checkpoint['model']['generator']		,					UpperCAmelCase__		,					UpperCAmelCase__      )

					__lowerCAmelCase					=							np.load(UpperCAmelCase__      )
					__lowerCAmelCase					=							stats[0].reshape(-1      )
					__lowerCAmelCase					=							stats[1].reshape(-1      )
					__lowerCAmelCase					=							torch.from_numpy(UpperCAmelCase__      ).float()
					__lowerCAmelCase					=							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__":
		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,
		)









from graphs.minimum_spanning_tree_kruskal import kruskal


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							9
					__lowerCAmelCase					=							[
					    [0, 1, 4],
					    [0, 7, 8],
					    [1, 2, 8],
					    [7, 8, 7],
					    [7, 6, 1],
					    [2, 8, 2],
					    [8, 6, 6],
					    [2, 3, 7],
					    [2, 5, 4],
					    [6, 5, 2],
					    [3, 5, 14],
					    [3, 4, 9],
					    [5, 4, 10],
					    [1, 7, 11],
					]

					__lowerCAmelCase					=							kruskal(UpperCAmelCase__		,					UpperCAmelCase__      )

					__lowerCAmelCase					=							[
					    [7, 6, 1],
					    [2, 8, 2],
					    [6, 5, 2],
					    [0, 1, 4],
					    [2, 5, 4],
					    [2, 3, 7],
					    [0, 7, 8],
					    [3, 4, 9],
					]

					assert sorted(UpperCAmelCase__      ) == sorted(UpperCAmelCase__      )






