from __future__ import annotations
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    List[Any]							=							len(__A						)
						# We need to create solution object to save path.
						SCREAMING_SNAKE_CASE							:    Dict							=							[[0 for _ in range(__A						)] for _ in range(__A						)]
						SCREAMING_SNAKE_CASE							:    int							=							run_maze(__A		,  0		,  0		,  __A						)
						if solved:
												print('''\n'''.join(str(__A						) for row in solutions						)						)
						else:
												print('''No solution exists!'''						)
						return solved
def       A	(    _lowercase		,  _lowercase		,  _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							len(__A						)
						# Final check point.
						if i == j == (size - 1):
												SCREAMING_SNAKE_CASE							:    List[Any]							=							1
												return True

						SCREAMING_SNAKE_CASE							:    List[str]							=							(not i < 0) and (not j < 0)  # Check lower bounds
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							(i < size) and (j < size)  # Check upper bounds

						if lower_flag and upper_flag:
												# check for already visited and block points.
												SCREAMING_SNAKE_CASE							:    List[str]							=							(not solutions[i][j]) and (not maze[i][j])
												if block_flag:
																		# check visited
																		SCREAMING_SNAKE_CASE							:    Tuple							=							1

																		# check for directions
																		if (
																		    run_maze(__A		,  i + 1		,  __A		,  __A						)
																		    or run_maze(__A		,  __A		,  j + 1		,  __A						)
																		    or run_maze(__A		,  i - 1		,  __A		,  __A						)
																		    or run_maze(__A		,  __A		,  j - 1		,  __A						)
																		):
																								return True

																		SCREAMING_SNAKE_CASE							:    Tuple							=							0
																		return False
						return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()







'''simple docstring'''







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowercase    :							List[str]      =					logging.get_logger("transformers.models.speecht5")







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A	)       ->	Dict:
	hf_model.apply_weight_norm()

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

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

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

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

	hf_model.remove_weight_norm()







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

	_snake_case   						=		SpeechTaHifiGan(__A	)

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

	_snake_case   						=		np.load(__A	)
	_snake_case   						=		stats[0].reshape(-1	)
	_snake_case   						=		stats[1].reshape(-1	)
	_snake_case   						=		torch.from_numpy(__A	).float()
	_snake_case   						=		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__":
					lowercase    :							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."
					)

					lowercase    :							List[Any]      =					parser.parse_args()
					convert_hifigan_checkpoint(
					    args.checkpoint_path,
					    args.stats_path,
					    args.pytorch_dump_folder_path,
					    args.config_path,
					    args.push_to_hub,
					)

