

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_lowerCAmelCase					:			Optional[Any]  				=			logging.get_logger("transformers.models.speecht5")






def        UpperCamelCase_(							_snake_case   :  List[Any]       ,     _snake_case   :  Tuple       ,     _snake_case   :  Optional[Any]	):




			"""simple docstring"""






			hf_model.apply_weight_norm()

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

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

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

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

			hf_model.remove_weight_norm()






@torch.no_grad()
def        UpperCamelCase_(							_snake_case   :  str       ,     _snake_case   :  int       ,     _snake_case   :  Optional[int]       ,     _snake_case   :  List[str]=None       ,     _snake_case   :  Optional[int]=None       ,     ):




			"""simple docstring"""






			if config_path is not None:
						__a        =SpeechTaHifiGanConfig.from_pretrained(_snake_case	)
			else:
						__a        =SpeechTaHifiGanConfig()

			__a        =SpeechTaHifiGan(_snake_case	)

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

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

			model.save_pretrained(_snake_case	)

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


if __name__ == "__main__":
						_lowerCAmelCase					:			Optional[int]  				=			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					:			Optional[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,
						)









# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.






def        UpperCamelCase_(							_snake_case   :  Tuple	):




			"""simple docstring"""






			__a        =[False] * len(_snake_case	)
			__a        =[-1] * len(_snake_case	)

			def dfs(_snake_case   :  Dict       ,     _snake_case   :  Any	):
						__a        =True
						__a        =c
						for u in graph[v]:
									if not visited[u]:
												dfs(_snake_case       ,     1 - c	)

			for i in range(len(_snake_case	)	):
						if not visited[i]:
									dfs(_snake_case       ,     0	)

			for i in range(len(_snake_case	)	):
						for j in graph[i]:
									if color[i] == color[j]:
												return False

			return True


# Adjacency list of graph
_lowerCAmelCase					:			int  				=			{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}


print(check_bipartite_dfs(graph))






