





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_lowercase		:  Tuple			       =logging.get_logger("""transformers.models.speecht5""")


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		hf_model.apply_weight_norm()

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

		for i in range(len(config.upsample_rates						)						):
				lowerCamelCase_	:     Any 						=      checkpoint[F"upsamples.{i}.1.weight_g"]
				lowerCamelCase_	:     List[str] 						=      checkpoint[F"upsamples.{i}.1.weight_v"]
				lowerCamelCase_	:     Optional[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						)						):
						lowerCamelCase_	:     Dict 						=      checkpoint[F"blocks.{i}.convs1.{j}.1.weight_g"]
						lowerCamelCase_	:     List[str] 						=      checkpoint[F"blocks.{i}.convs1.{j}.1.weight_v"]
						lowerCamelCase_	:     Tuple 						=      checkpoint[F"blocks.{i}.convs1.{j}.1.bias"]

						lowerCamelCase_	:     Tuple 						=      checkpoint[F"blocks.{i}.convs2.{j}.1.weight_g"]
						lowerCamelCase_	:     Any 						=      checkpoint[F"blocks.{i}.convs2.{j}.1.weight_v"]
						lowerCamelCase_	:     Union[str, Any] 						=      checkpoint[F"blocks.{i}.convs2.{j}.1.bias"]

		lowerCamelCase_	:     Tuple 						=      checkpoint['output_conv.1.weight_g']
		lowerCamelCase_	:     int 						=      checkpoint['output_conv.1.weight_v']
		lowerCamelCase_	:     Optional[int] 						=      checkpoint['output_conv.1.bias']

		hf_model.remove_weight_norm()


@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__=None							,lowerCAmelCase__=None							,):
		if config_path is not None:
				lowerCamelCase_	:     Tuple 						=      SpeechTaHifiGanConfig.from_pretrained(lowerCAmelCase__						)
		else:
				lowerCamelCase_	:     List[Any] 						=      SpeechTaHifiGanConfig()

		lowerCamelCase_	:     List[str] 						=      SpeechTaHifiGan(lowerCAmelCase__						)

		lowerCamelCase_	:     Optional[int] 						=      torch.load(lowerCAmelCase__						)
		load_weights(orig_checkpoint['model']['generator']							,lowerCAmelCase__							,lowerCAmelCase__						)

		lowerCamelCase_	:     Optional[Any] 						=      np.load(lowerCAmelCase__						)
		lowerCamelCase_	:     Union[str, Any] 						=      stats[0].reshape(-1						)
		lowerCamelCase_	:     Dict 						=      stats[1].reshape(-1						)
		lowerCamelCase_	:     Any 						=      torch.from_numpy(lowerCAmelCase__						).float()
		lowerCamelCase_	:     str 						=      torch.from_numpy(lowerCAmelCase__						).float()

		model.save_pretrained(lowerCAmelCase__						)

		if repo_id:
				print('Pushing to the hub...'						)
				model.push_to_hub(lowerCAmelCase__						)


if __name__ == "__main__":
		_lowercase		:  List[Any]			       =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		:  Union[str, 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,
		)







from __future__ import annotations

import math


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):

		if num <= 0:
				lowerCamelCase_	:     Optional[int] 						=      F"{num}: Invalid input, please enter a positive integer."
				raise ValueError(lowerCAmelCase__						)

		lowerCamelCase_	:     str 						=      [True] * (num + 1)
		lowerCamelCase_	:     List[str] 						=      []
		lowerCamelCase_	:     Optional[int] 						=      2
		lowerCamelCase_	:     List[str] 						=      int(math.sqrt(lowerCAmelCase__						)						)

		while start <= end:
				# If start is a prime
				if sieve[start] is True:
						prime.append(lowerCAmelCase__						)

						# Set multiples of start be False
						for i in range(start * start							,num + 1							,lowerCAmelCase__						):
								if sieve[i] is True:
										lowerCamelCase_	:     Tuple 						=      False

				start += 1

		for j in range(end + 1							,num + 1						):
				if sieve[j] is True:
						prime.append(lowerCAmelCase__						)

		return prime


if __name__ == "__main__":
		print(prime_sieve(int(input("""Enter a positive integer: """).strip())))
