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 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case						):
					hf_model.apply_weight_norm()

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

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

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

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

					hf_model.remove_weight_norm()
@torch.no_grad()
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case=None  ,     __snake_case=None  ,     ):
					if config_path is not None:
										_UpperCamelCase			     =     SpeechTaHifiGanConfig.from_pretrained(__snake_case						)
					else:
										_UpperCamelCase			     =     SpeechTaHifiGanConfig()

					_UpperCamelCase			     =     SpeechTaHifiGan(__snake_case						)

					_UpperCamelCase			     =     torch.load(__snake_case						)
					load_weights(orig_checkpoint['''model''']['''generator''']  ,     __snake_case  ,     __snake_case						)

					_UpperCamelCase			     =     np.load(__snake_case						)
					_UpperCamelCase			     =     stats[0].reshape(-1						)
					_UpperCamelCase			     =     stats[1].reshape(-1						)
					_UpperCamelCase			     =     torch.from_numpy(__snake_case						).float()
					_UpperCamelCase			     =     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       =		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,
					)





def     UpperCAmelCase_		(					)	->     int:



				"""simple docstring"""





				return 1







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





				return 0 if x < 0 else two_pence(x - 2					) + one_pence()







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





				return 0 if x < 0 else five_pence(x - 5					) + two_pence(snake_case__					)







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





				return 0 if x < 0 else ten_pence(x - 10					) + five_pence(snake_case__					)







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





				return 0 if x < 0 else twenty_pence(x - 20					) + ten_pence(snake_case__					)







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





				return 0 if x < 0 else fifty_pence(x - 50					) + twenty_pence(snake_case__					)







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





				return 0 if x < 0 else one_pound(x - 100					) + fifty_pence(snake_case__					)







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





				return 0 if x < 0 else two_pound(x - 200					) + one_pound(snake_case__					)







def     UpperCAmelCase_		(					snake_case__ = 200					)	->     int:



				"""simple docstring"""





				return two_pound(snake_case__					)


if __name__ == "__main__":
	print(solution(int(input().strip())))
