



'''simple docstring'''




import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__	:     Optional[Any]    =			logging.get_logger(__name__)


def     lowerCAmelCase_       (     _lowerCamelCase:       Optional[Any] ):
		__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		ConvNextVaConfig()

		if "atto" in checkpoint_url:
				__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		[2, 2, 6, 2]
				__SCREAMING_SNAKE_CASE	:		Any   	=		[40, 80, 1_60, 3_20]
		if "femto" in checkpoint_url:
				__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		[2, 2, 6, 2]
				__SCREAMING_SNAKE_CASE	:		str   	=		[48, 96, 1_92, 3_84]
		if "pico" in checkpoint_url:
				__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		[2, 2, 6, 2]
				__SCREAMING_SNAKE_CASE	:		int   	=		[64, 1_28, 2_56, 5_12]
		if "nano" in checkpoint_url:
				__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		[2, 2, 8, 2]
				__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		[80, 1_60, 3_20, 6_40]
		if "tiny" in checkpoint_url:
				__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		[3, 3, 9, 3]
				__SCREAMING_SNAKE_CASE	:		int   	=		[96, 1_92, 3_84, 7_68]
		if "base" in checkpoint_url:
				__SCREAMING_SNAKE_CASE	:		Any   	=		[3, 3, 27, 3]
				__SCREAMING_SNAKE_CASE	:		str   	=		[1_28, 2_56, 5_12, 10_24]
		if "large" in checkpoint_url:
				__SCREAMING_SNAKE_CASE	:		Any   	=		[3, 3, 27, 3]
				__SCREAMING_SNAKE_CASE	:		str   	=		[1_92, 3_84, 7_68, 15_36]
		if "huge" in checkpoint_url:
				__SCREAMING_SNAKE_CASE	:		List[Any]   	=		[3, 3, 27, 3]
				__SCREAMING_SNAKE_CASE	:		Dict   	=		[3_52, 7_04, 14_08, 28_16]

		__SCREAMING_SNAKE_CASE	:		str   	=		10_00
		__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		"""imagenet-1k-id2label.json"""
		__SCREAMING_SNAKE_CASE	:		int   	=		(1, 10_00)

		__SCREAMING_SNAKE_CASE	:		Any   	=		"""huggingface/label-files"""
		__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		num_labels
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		json.load(open(hf_hub_download(_lowerCamelCase	,				_lowerCamelCase	,				repo_type="""dataset""" )	,				"""r""" ) )
		__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		{int(_lowerCamelCase ): v for k, v in idalabel.items()}

		__SCREAMING_SNAKE_CASE	:		List[str]   	=		idalabel
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		{v: k for k, v in idalabel.items()}
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		hidden_sizes
		__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		depths

		return config, expected_shape


def     lowerCAmelCase_       (     _lowerCamelCase:       Union[str, Any] ):
		if "downsample_layers.0.0" in name:
				__SCREAMING_SNAKE_CASE	:		List[str]   	=		name.replace("""downsample_layers.0.0"""	,				"""embeddings.patch_embeddings""" )
		if "downsample_layers.0.1" in name:
				__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		name.replace("""downsample_layers.0.1"""	,				"""embeddings.norm""" )  # we rename to layernorm later on
		if "downsample_layers.1.0" in name:
				__SCREAMING_SNAKE_CASE	:		int   	=		name.replace("""downsample_layers.1.0"""	,				"""stages.1.downsampling_layer.0""" )
		if "downsample_layers.1.1" in name:
				__SCREAMING_SNAKE_CASE	:		List[str]   	=		name.replace("""downsample_layers.1.1"""	,				"""stages.1.downsampling_layer.1""" )
		if "downsample_layers.2.0" in name:
				__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		name.replace("""downsample_layers.2.0"""	,				"""stages.2.downsampling_layer.0""" )
		if "downsample_layers.2.1" in name:
				__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		name.replace("""downsample_layers.2.1"""	,				"""stages.2.downsampling_layer.1""" )
		if "downsample_layers.3.0" in name:
				__SCREAMING_SNAKE_CASE	:		Any   	=		name.replace("""downsample_layers.3.0"""	,				"""stages.3.downsampling_layer.0""" )
		if "downsample_layers.3.1" in name:
				__SCREAMING_SNAKE_CASE	:		str   	=		name.replace("""downsample_layers.3.1"""	,				"""stages.3.downsampling_layer.1""" )
		if "stages" in name and "downsampling_layer" not in name:
				# stages.0.0. for instance should be renamed to stages.0.layers.0.
				__SCREAMING_SNAKE_CASE	:		Any   	=		name[: len("""stages.0""" )] + """.layers""" + name[len("""stages.0""" ) :]
		if "gamma" in name:
				__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		name.replace("""gamma"""	,				"""weight""" )
		if "beta" in name:
				__SCREAMING_SNAKE_CASE	:		List[Any]   	=		name.replace("""beta"""	,				"""bias""" )
		if "stages" in name:
				__SCREAMING_SNAKE_CASE	:		int   	=		name.replace("""stages"""	,				"""encoder.stages""" )
		if "norm" in name:
				__SCREAMING_SNAKE_CASE	:		Dict   	=		name.replace("""norm"""	,				"""layernorm""" )
		if "head" in name:
				__SCREAMING_SNAKE_CASE	:		Any   	=		name.replace("""head"""	,				"""classifier""" )

		return name


def     lowerCAmelCase_       (     ):
		__SCREAMING_SNAKE_CASE	:		int   	=		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
		__SCREAMING_SNAKE_CASE	:		str   	=		Image.open(requests.get(_lowerCamelCase	,				stream=_lowerCamelCase ).raw )
		return im


def     lowerCAmelCase_       (     _lowerCamelCase:       Optional[Any] ):
		if "224" in checkpoint_url:
				__SCREAMING_SNAKE_CASE	:		List[str]   	=		2_24
				__SCREAMING_SNAKE_CASE	:		Any   	=		2_24 / 2_56
		elif "384" in checkpoint_url:
				__SCREAMING_SNAKE_CASE	:		Tuple   	=		3_84
				__SCREAMING_SNAKE_CASE	:		Any   	=		None
		else:
				__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		5_12
				__SCREAMING_SNAKE_CASE	:		Dict   	=		None

		return ConvNextImageProcessor(
		    size=_lowerCamelCase	,				crop_pct=_lowerCamelCase	,				image_mean=[0.4_85, 0.4_56, 0.4_06]	,				image_std=[0.2_29, 0.2_24, 0.2_25]	,				resample=PILImageResampling.BICUBIC	,				)


@torch.no_grad()
def     lowerCAmelCase_       (     _lowerCamelCase:       Optional[int]	,				_lowerCamelCase:       List[Any]	,				_lowerCamelCase:       Optional[Any]	,				_lowerCamelCase:       List[Any] ):
		print("""Downloading original model from checkpoint...""" )
		# define ConvNeXTV2 configuration based on URL
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		get_convnextva_config(_lowerCamelCase )
		# load original state_dict from URL
		__SCREAMING_SNAKE_CASE	:		Dict   	=		torch.hub.load_state_dict_from_url(_lowerCamelCase )["""model"""]

		print("""Converting model parameters...""" )
		# rename keys
		for key in state_dict.copy().keys():
				__SCREAMING_SNAKE_CASE	:		List[Any]   	=		state_dict.pop(_lowerCamelCase )
				__SCREAMING_SNAKE_CASE	:		List[Any]   	=		val
		# add prefix to all keys expect classifier head
		for key in state_dict.copy().keys():
				__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		state_dict.pop(_lowerCamelCase )
				if not key.startswith("""classifier""" ):
						__SCREAMING_SNAKE_CASE	:		str   	=		"""convnextv2.""" + key
				__SCREAMING_SNAKE_CASE	:		Tuple   	=		val

		# load HuggingFace model
		__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		ConvNextVaForImageClassification(_lowerCamelCase )
		model.load_state_dict(_lowerCamelCase )
		model.eval()

		# Check outputs on an image, prepared by ConvNextImageProcessor
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		convert_preprocessor(_lowerCamelCase )
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		preprocessor(images=prepare_img()	,				return_tensors="""pt""" )
		__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		model(**_lowerCamelCase ).logits

		# note: the logits below were obtained without center cropping
		if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
				__SCREAMING_SNAKE_CASE	:		Tuple   	=		torch.tensor([-0.39_30, 0.17_47, -0.52_46, 0.41_77, 0.42_95] )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
				__SCREAMING_SNAKE_CASE	:		Any   	=		torch.tensor([-0.17_27, -0.53_41, -0.78_18, -0.47_45, -0.65_66] )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
				__SCREAMING_SNAKE_CASE	:		List[Any]   	=		torch.tensor([-0.03_33, 0.15_63, -0.91_37, 0.10_54, 0.03_81] )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
				__SCREAMING_SNAKE_CASE	:		Any   	=		torch.tensor([-0.17_44, -0.15_55, -0.07_13, 0.09_50, -0.14_31] )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
				__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		torch.tensor([0.99_96, 0.19_66, -0.43_86, -0.34_72, 0.66_61] )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
				__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		torch.tensor([-0.25_53, -0.67_08, -0.13_59, 0.25_18, -0.24_88] )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
				__SCREAMING_SNAKE_CASE	:		List[Any]   	=		torch.tensor([-0.06_73, -0.56_27, -0.37_53, -0.27_22, 0.01_78] )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
				__SCREAMING_SNAKE_CASE	:		str   	=		torch.tensor([-0.63_77, -0.74_58, -0.21_50, 0.11_84, -0.05_97] )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
				__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		torch.tensor([1.07_99, 0.23_22, -0.88_60, 1.02_19, 0.62_31] )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
				__SCREAMING_SNAKE_CASE	:		str   	=		torch.tensor([0.37_66, 0.49_17, -1.14_26, 0.99_42, 0.60_24] )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
				__SCREAMING_SNAKE_CASE	:		List[Any]   	=		torch.tensor([0.42_20, -0.69_19, -0.43_17, -0.28_81, -0.66_09] )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
				__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		torch.tensor([0.10_82, -0.82_86, -0.50_95, 0.46_81, -0.80_85] )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
				__SCREAMING_SNAKE_CASE	:		Dict   	=		torch.tensor([-0.24_19, -0.62_21, 0.21_76, -0.09_80, -0.75_27] )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
				__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		torch.tensor([0.03_91, -0.43_71, 0.37_86, 0.12_51, -0.27_84] )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
				__SCREAMING_SNAKE_CASE	:		List[Any]   	=		torch.tensor([-0.05_04, 0.56_36, -0.17_29, -0.65_07, -0.39_49] )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
				__SCREAMING_SNAKE_CASE	:		Dict   	=		torch.tensor([0.35_60, 0.94_86, 0.31_49, -0.26_67, -0.51_38] )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
				__SCREAMING_SNAKE_CASE	:		int   	=		torch.tensor([-0.24_69, -0.45_50, -0.58_53, -0.08_10, 0.03_09] )
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
				__SCREAMING_SNAKE_CASE	:		List[Any]   	=		torch.tensor([-0.30_90, 0.08_02, -0.06_82, -0.19_79, -0.28_26] )
		else:
				raise ValueError(F"Unknown URL: {checkpoint_url}" )

		assert torch.allclose(logits[0, :5]	,				_lowerCamelCase	,				atol=1E-3 )
		assert logits.shape == expected_shape
		print("""Model outputs match the original results!""" )

		if save_model:
				print("""Saving model to local...""" )
				# Create folder to save model
				if not os.path.isdir(_lowerCamelCase ):
						os.mkdir(_lowerCamelCase )

				model.save_pretrained(_lowerCamelCase )
				preprocessor.save_pretrained(_lowerCamelCase )

		__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		"""convnextv2"""
		if "atto" in checkpoint_url:
				model_name += "-atto"
		if "femto" in checkpoint_url:
				model_name += "-femto"
		if "pico" in checkpoint_url:
				model_name += "-pico"
		if "nano" in checkpoint_url:
				model_name += "-nano"
		elif "tiny" in checkpoint_url:
				model_name += "-tiny"
		elif "base" in checkpoint_url:
				model_name += "-base"
		elif "large" in checkpoint_url:
				model_name += "-large"
		elif "huge" in checkpoint_url:
				model_name += "-huge"
		if "22k" in checkpoint_url and "1k" not in checkpoint_url:
				model_name += "-22k"
		elif "22k" in checkpoint_url and "1k" in checkpoint_url:
				model_name += "-22k-1k"
		elif "1k" in checkpoint_url:
				model_name += "-1k"
		if "224" in checkpoint_url:
				model_name += "-224"
		elif "384" in checkpoint_url:
				model_name += "-384"
		elif "512" in checkpoint_url:
				model_name += "-512"

		if push_to_hub:
				print(F"Pushing {model_name} to the hub..." )
				model.push_to_hub(_lowerCamelCase )
				preprocessor.push_to_hub(_lowerCamelCase )


if __name__ == "__main__":
						UpperCamelCase__	:     Tuple    =			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--checkpoint_url''',
						    default='''https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt''',
						    type=str,
						    help='''URL of the original ConvNeXTV2 checkpoint you\'d like to convert.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''',
						    default='''model''',
						    type=str,
						    help='''Path to the output PyTorch model directory.''',
						)
						parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
						parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image preprocessor to the hub''')

						UpperCamelCase__	:     Tuple    =			parser.parse_args()
						convert_convnextva_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
						)




'''simple docstring'''




import os
import sys

from . import rsa_key_generator as rkg

UpperCamelCase__	:     Any    =			1_28
UpperCamelCase__	:     Optional[int]    =			2_56


def     lowerCAmelCase_       (     _lowerCamelCase:       str	,				_lowerCamelCase:       int = DEFAULT_BLOCK_SIZE ):
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		message.encode("""ascii""" )

		__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		[]
		for block_start in range(0	,				len(_lowerCamelCase )	,				_lowerCamelCase ):
				__SCREAMING_SNAKE_CASE	:		str   	=		0
				for i in range(_lowerCamelCase	,				min(block_start + block_size	,				len(_lowerCamelCase ) ) ):
						block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
				block_ints.append(_lowerCamelCase )
		return block_ints


def     lowerCAmelCase_       (     _lowerCamelCase:       list[int]	,				_lowerCamelCase:       int	,				_lowerCamelCase:       int = DEFAULT_BLOCK_SIZE ):
		__SCREAMING_SNAKE_CASE	:		list[str]   	=		[]
		for block_int in block_ints:
				__SCREAMING_SNAKE_CASE	:		list[str]   	=		[]
				for i in range(block_size - 1	,				-1	,				-1 ):
						if len(_lowerCamelCase ) + i < message_length:
								__SCREAMING_SNAKE_CASE	:		List[Any]   	=		block_int // (BYTE_SIZE**i)
								__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		block_int % (BYTE_SIZE**i)
								block_message.insert(0	,				chr(_lowerCamelCase ) )
				message.extend(_lowerCamelCase )
		return "".join(_lowerCamelCase )


def     lowerCAmelCase_       (     _lowerCamelCase:       str	,				_lowerCamelCase:       tuple[int, int]	,				_lowerCamelCase:       int = DEFAULT_BLOCK_SIZE ):
		__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		[]
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		key

		for block in get_blocks_from_text(_lowerCamelCase	,				_lowerCamelCase ):
				encrypted_blocks.append(pow(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase ) )
		return encrypted_blocks


def     lowerCAmelCase_       (     _lowerCamelCase:       list[int]	,				_lowerCamelCase:       int	,				_lowerCamelCase:       tuple[int, int]	,				_lowerCamelCase:       int = DEFAULT_BLOCK_SIZE	,				):
		__SCREAMING_SNAKE_CASE	:		Dict   	=		[]
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		key
		for block in encrypted_blocks:
				decrypted_blocks.append(pow(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase ) )
		return get_text_from_blocks(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase )


def     lowerCAmelCase_       (     _lowerCamelCase:       str ):
		with open(_lowerCamelCase ) as fo:
				__SCREAMING_SNAKE_CASE	:		Tuple   	=		fo.read()
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		int   	=		content.split(""",""" )
		return (int(_lowerCamelCase ), int(_lowerCamelCase ), int(_lowerCamelCase ))


def     lowerCAmelCase_       (     _lowerCamelCase:       str	,				_lowerCamelCase:       str	,				_lowerCamelCase:       str	,				_lowerCamelCase:       int = DEFAULT_BLOCK_SIZE	,				):
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Tuple   	=		read_key_file(_lowerCamelCase )
		if key_size < block_size * 8:
				sys.exit(
				    """ERROR: Block size is {} bits and key size is {} bits. The RSA cipher """
				    """requires the block size to be equal to or greater than the key size. """
				    """Either decrease the block size or use different keys.""".format(
				        block_size * 8	,				_lowerCamelCase ) )

		__SCREAMING_SNAKE_CASE	:		Dict   	=		[str(_lowerCamelCase ) for i in encrypt_message(_lowerCamelCase	,				(n, e)	,				_lowerCamelCase )]

		__SCREAMING_SNAKE_CASE	:		int   	=		""",""".join(_lowerCamelCase )
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		F"{len(_lowerCamelCase )}_{block_size}_{encrypted_content}"
		with open(_lowerCamelCase	,				"""w""" ) as fo:
				fo.write(_lowerCamelCase )
		return encrypted_content


def     lowerCAmelCase_       (     _lowerCamelCase:       str	,				_lowerCamelCase:       str ):
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Tuple   	=		read_key_file(_lowerCamelCase )
		with open(_lowerCamelCase ) as fo:
				__SCREAMING_SNAKE_CASE	:		Any   	=		fo.read()
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		content.split("""_""" )
		__SCREAMING_SNAKE_CASE	:		int   	=		int(_lowerCamelCase )
		__SCREAMING_SNAKE_CASE	:		int   	=		int(_lowerCamelCase )

		if key_size < block_size * 8:
				sys.exit(
				    """ERROR: Block size is {} bits and key size is {} bits. The RSA cipher """
				    """requires the block size to be equal to or greater than the key size. """
				    """Did you specify the correct key file and encrypted file?""".format(
				        block_size * 8	,				_lowerCamelCase ) )

		__SCREAMING_SNAKE_CASE	:		Dict   	=		[]
		for block in encrypted_message.split(""",""" ):
				encrypted_blocks.append(int(_lowerCamelCase ) )

		return decrypt_message(_lowerCamelCase	,				_lowerCamelCase	,				(n, d)	,				_lowerCamelCase )


def     lowerCAmelCase_       (     ):
		__SCREAMING_SNAKE_CASE	:		Dict   	=		"""encrypted_file.txt"""
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		input(r"""Encrypt\Decrypt [e\d]: """ )

		if response.lower().startswith("""e""" ):
				__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		"""encrypt"""
		elif response.lower().startswith("""d""" ):
				__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		"""decrypt"""

		if mode == "encrypt":
				if not os.path.exists("""rsa_pubkey.txt""" ):
						rkg.make_key_files("""rsa"""	,				10_24 )

				__SCREAMING_SNAKE_CASE	:		int   	=		input("""\nEnter message: """ )
				__SCREAMING_SNAKE_CASE	:		str   	=		"""rsa_pubkey.txt"""
				print(F"Encrypting and writing to {filename}..." )
				__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		encrypt_and_write_to_file(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase )

				print("""\nEncrypted text:""" )
				print(_lowerCamelCase )

		elif mode == "decrypt":
				__SCREAMING_SNAKE_CASE	:		List[str]   	=		"""rsa_privkey.txt"""
				print(F"Reading from {filename} and decrypting..." )
				__SCREAMING_SNAKE_CASE	:		List[str]   	=		read_from_file_and_decrypt(_lowerCamelCase	,				_lowerCamelCase )
				print("""writing decryption to rsa_decryption.txt...""" )
				with open("""rsa_decryption.txt"""	,				"""w""" ) as dec:
						dec.write(_lowerCamelCase )

				print("""\nDecryption:""" )
				print(_lowerCamelCase )


if __name__ == "__main__":
						main()