from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

__UpperCamelCase		:       str									=     (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__UpperCamelCase		:       list[int]									=     [ord(letter) for letter in string.ascii_lowercase]
__UpperCamelCase		:       set[int]									=     {ord(char) for char in VALID_CHARS}

__UpperCamelCase		:       list[str]									=     ["the", "be", "to", "of", "and", "in", "that", "have"]
def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    List[str]							=							''''''
						SCREAMING_SNAKE_CASE							:    List[Any]							=							42
						SCREAMING_SNAKE_CASE							:    List[str]							=							42
						SCREAMING_SNAKE_CASE							:    Tuple							=							42

						for keychar, cipherchar in zip(cycle(__A						)		,  __A						):
												SCREAMING_SNAKE_CASE							:    int							=							cipherchar ^ keychar
												if decodedchar not in VALID_INTS:
																		return None
												decoded += chr(__A						)

						return decoded
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    Tuple							=							[]
						for key in product(__A		,  repeat=3						):
												SCREAMING_SNAKE_CASE							:    Any							=							try_key(__A		,  __A						)
												if encoded is not None:
																		possibles.append(__A						)
						return possibles
def       A	(    _lowercase		,  _lowercase						):
						return [possible for possible in possibles if common_word in possible.lower()]
def       A	(    _lowercase = "p059_cipher.txt"						):
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							42
						SCREAMING_SNAKE_CASE							:    Any							=							42
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							42
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							42
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							Path(__A						).parent.joinpath(__A						).read_text(encoding='''utf-8'''						)

						SCREAMING_SNAKE_CASE							:    Optional[int]							=							[int(__A						) for number in data.strip().split(''','''						)]

						SCREAMING_SNAKE_CASE							:    List[str]							=							filter_valid_chars(__A						)
						for common_word in COMMON_WORDS:
												SCREAMING_SNAKE_CASE							:    Tuple							=							filter_common_word(__A		,  __A						)
												if len(__A						) == 1:
																		break

						SCREAMING_SNAKE_CASE							:    str							=							possibles[0]
						return sum(ord(__A						) for char in decoded_text						)


if __name__ == "__main__":
					print(f"""{solution() = }""")







'''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,
					)

