



'''simple docstring'''
import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
snake_case_					:      Any    			=    logging.get_logger('transformers.models.speecht5')






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						hf_model.apply_weight_norm()

						_UpperCamelCase   :						Any												=  checkpoint['input_conv.weight_g']
						_UpperCamelCase   :						str												=  checkpoint['input_conv.weight_v']
						_UpperCamelCase   :						Union[str, Any]												=  checkpoint['input_conv.bias']

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

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

						_UpperCamelCase   :						Any												=  checkpoint['output_conv.1.weight_g']
						_UpperCamelCase   :						str												=  checkpoint['output_conv.1.weight_v']
						_UpperCamelCase   :						int												=  checkpoint['output_conv.1.bias']

						hf_model.remove_weight_norm()






@torch.no_grad()
def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_=None    ,    UpperCAmelCase_=None    ,    ):
						if config_path is not None:
												_UpperCamelCase   :						List[Any]												=  SpeechTaHifiGanConfig.from_pretrained(UpperCAmelCase_				)
						else:
												_UpperCamelCase   :						List[Any]												=  SpeechTaHifiGanConfig()

						_UpperCamelCase   :						Optional[int]												=  SpeechTaHifiGan(UpperCAmelCase_				)

						_UpperCamelCase   :						Union[str, Any]												=  torch.load(UpperCAmelCase_				)
						load_weights(orig_checkpoint['model']['generator']    ,    UpperCAmelCase_    ,    UpperCAmelCase_				)

						_UpperCamelCase   :						str												=  np.load(UpperCAmelCase_				)
						_UpperCamelCase   :						Tuple												=  stats[0].reshape(-1				)
						_UpperCamelCase   :						Union[str, Any]												=  stats[1].reshape(-1				)
						_UpperCamelCase   :						Optional[int]												=  torch.from_numpy(UpperCAmelCase_				).float()
						_UpperCamelCase   :						List[str]												=  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__":
					snake_case_					:      Dict    			=    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.'
					)

					snake_case_					:      str    			=    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 A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase							,     _UpperCamelCase   :						Dict												=  position
						_UpperCamelCase   :						Any												=  [
						    (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),
						]
						_UpperCamelCase   :						Optional[Any]												=  []

						for position in positions:
												_UpperCamelCase							,     _UpperCamelCase   :						Any												=  position
												if 0 <= y_test < n and 0 <= x_test < n:
																		permissible_positions.append(UpperCAmelCase_				)

						return permissible_positions






def A__       (	UpperCAmelCase_				):
						return not any(elem == 0 for row in board for elem in row				)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):

						if is_complete(UpperCAmelCase_				):
												return True

						for position in get_valid_pos(UpperCAmelCase_    ,    len(UpperCAmelCase_				)				):
												_UpperCamelCase							,     _UpperCamelCase   :						Any												=  position

												if board[y][x] == 0:
																		_UpperCamelCase   :						int												=  curr + 1
																		if open_knight_tour_helper(UpperCAmelCase_    ,    UpperCAmelCase_    ,    curr + 1				):
																								return True
																		_UpperCamelCase   :						Union[str, Any]												=  0

						return False






def A__       (	UpperCAmelCase_				):
						_UpperCamelCase   :						Dict												=  [[0 for i in range(UpperCAmelCase_				)] for j in range(UpperCAmelCase_				)]

						for i in range(UpperCAmelCase_				):
												for j in range(UpperCAmelCase_				):
																		_UpperCamelCase   :						Tuple												=  1
																		if open_knight_tour_helper(UpperCAmelCase_    ,    (i, j)    ,    1				):
																								return board
																		_UpperCamelCase   :						Union[str, Any]												=  0

						_UpperCamelCase   :						int												=  f'Open Kight Tour cannot be performed on a board of size {n}'
						raise ValueError(UpperCAmelCase_				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


