






'''simple docstring'''







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 		_UpperCAmelCase   (							__A							:   Dict						,		__A							:   List[Any]						,		__A							:   List[Any]      ):
				hf_model.apply_weight_norm()

				a_		:  List[Any]											=      checkpoint['''input_conv.weight_g''']
				a_		:  Optional[Any]											=      checkpoint['''input_conv.weight_v''']
				a_		:  Dict											=      checkpoint['''input_conv.bias''']

				for i in range(len(config.upsample_rates      )      ):
								a_		:  Optional[int]											=      checkpoint[f'upsamples.{i}.1.weight_g']
								a_		:  int											=      checkpoint[f'upsamples.{i}.1.weight_v']
								a_		:  Union[str, Any]											=      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_		:  Union[str, Any]											=      checkpoint[f'blocks.{i}.convs1.{j}.1.weight_g']
												a_		:  int											=      checkpoint[f'blocks.{i}.convs1.{j}.1.weight_v']
												a_		:  int											=      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_		:  Any											=      checkpoint[f'blocks.{i}.convs2.{j}.1.bias']

				a_		:  Union[str, Any]											=      checkpoint['''output_conv.1.weight_g''']
				a_		:  Optional[int]											=      checkpoint['''output_conv.1.weight_v''']
				a_		:  str											=      checkpoint['''output_conv.1.bias''']

				hf_model.remove_weight_norm()





@torch.no_grad()
def 		_UpperCAmelCase   (							__A							:   Any						,		__A							:   List[Any]						,		__A							:   Tuple						,		__A							:   Optional[int]=None						,		__A							:   Union[str, Any]=None						,		):
				if config_path is not None:
								a_		:  Dict											=      SpeechTaHifiGanConfig.from_pretrained(__A      )
				else:
								a_		:  int											=      SpeechTaHifiGanConfig()

				a_		:  Any											=      SpeechTaHifiGan(__A      )

				a_		:  Optional[Any]											=      torch.load(__A      )
				load_weights(orig_checkpoint['''model''']['''generator''']						,		__A						,		__A      )

				a_		:  Union[str, Any]											=      np.load(__A      )
				a_		:  str											=      stats[0].reshape(-1      )
				a_		:  Optional[Any]											=      stats[1].reshape(-1      )
				a_		:  Any											=      torch.from_numpy(__A      ).float()
				a_		:  str											=      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__":
		__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,
		)












'''simple docstring'''







from __future__ import annotations





def 		_UpperCAmelCase   (							__A							:   tuple[int, int]						,		__A							:   int      ):
				a_				,			a_		:  List[str]											=      position
				a_		:  Optional[int]											=      [
				    (y + 1, x + 2),
				    (y - 1, x + 2),
				    (y + 1, x - 2),
				    (y - 1, x - 2),
				    (y + 2, x + 1),
				    (y + 2, x - 1),
				    (y - 2, x + 1),
				    (y - 2, x - 1),
				]
				a_		:  Any											=      []

				for position in positions:
								a_				,			a_		:  Dict											=      position
								if 0 <= y_test < n and 0 <= x_test < n:
												permissible_positions.append(__A      )

				return permissible_positions





def 		_UpperCAmelCase   (							__A							:   list[list[int]]      ):
				return not any(elem == 0 for row in board for elem in row      )





def 		_UpperCAmelCase   (							__A							:   list[list[int]]						,		__A							:   tuple[int, int]						,		__A							:   int      ):

				if is_complete(__A      ):
								return True

				for position in get_valid_pos(__A						,		len(__A      )      ):
								a_				,			a_		:  Dict											=      position

								if board[y][x] == 0:
												a_		:  Optional[Any]											=      curr + 1
												if open_knight_tour_helper(__A						,		__A						,		curr + 1      ):
																return True
												a_		:  Tuple											=      0

				return False





def 		_UpperCAmelCase   (							__A							:   int      ):
				a_		:  List[str]											=      [[0 for i in range(__A      )] for j in range(__A      )]

				for i in range(__A      ):
								for j in range(__A      ):
												a_		:  Optional[Any]											=      1
												if open_knight_tour_helper(__A						,		(i, j)						,		1      ):
																return board
												a_		:  Union[str, Any]											=      0

				a_		:  Dict											=      f'Open Kight Tour cannot be performed on a board of size {n}'
				raise ValueError(__A      )


if __name__ == "__main__":
		import doctest

		doctest.testmod()




