
import os
import sys

from . import rsa_key_generator as rkg

lowercase__    :					Union[str, Any]					=						128
lowercase__    :					List[str]					=						256


def 						lowerCamelCase__   (		_A					,     _A = DEFAULT_BLOCK_SIZE							):







	'''simple docstring'''





	snake_case_		   =					message.encode("ascii"							)

	snake_case_		   =					[]
	for block_start in range(0					,     len(_A							)					,     _A							):
		snake_case_		   =					0
		for i in range(_A					,     min(block_start + block_size					,     len(_A							)							)							):
			block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
		block_ints.append(_A							)
	return block_ints


def 						lowerCamelCase__   (		_A					,     _A					,     _A = DEFAULT_BLOCK_SIZE							):







	'''simple docstring'''





	snake_case_		   =					[]
	for block_int in block_ints:
		snake_case_		   =					[]
		for i in range(block_size - 1					,     -1					,     -1							):
			if len(_A							) + i < message_length:
				snake_case_		   =					block_int // (BYTE_SIZE**i)
				snake_case_		   =					block_int % (BYTE_SIZE**i)
				block_message.insert(0					,     chr(_A							)							)
		message.extend(_A							)
	return "".join(_A							)


def 						lowerCamelCase__   (		_A					,     _A					,     _A = DEFAULT_BLOCK_SIZE							):







	'''simple docstring'''





	snake_case_		   =					[]
	snake_case_   ,				snake_case_		   =					key

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


def 						lowerCamelCase__   (		_A					,     _A					,     _A					,     _A = DEFAULT_BLOCK_SIZE					,     ):







	'''simple docstring'''





	snake_case_		   =					[]
	snake_case_   ,				snake_case_		   =					key
	for block in encrypted_blocks:
		decrypted_blocks.append(pow(_A					,     _A					,     _A							)							)
	return get_text_from_blocks(_A					,     _A					,     _A							)


def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





	with open(_A							) as fo:
		snake_case_		   =					fo.read()
	snake_case_   ,				snake_case_   ,				snake_case_		   =					content.split(","							)
	return (int(_A							), int(_A							), int(_A							))


def 						lowerCamelCase__   (		_A					,     _A					,     _A					,     _A = DEFAULT_BLOCK_SIZE					,     ):







	'''simple docstring'''





	snake_case_   ,				snake_case_   ,				snake_case_		   =					read_key_file(_A							)
	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					,     _A							)							)

	snake_case_		   =					[str(_A							) for i in encrypt_message(_A					,     (n, e)					,     _A							)]

	snake_case_		   =					",".join(_A							)
	snake_case_		   =					f"{len(_A							)}_{block_size}_{encrypted_content}"
	with open(_A					,     "w"							) as fo:
		fo.write(_A							)
	return encrypted_content


def 						lowerCamelCase__   (		_A					,     _A							):







	'''simple docstring'''





	snake_case_   ,				snake_case_   ,				snake_case_		   =					read_key_file(_A							)
	with open(_A							) as fo:
		snake_case_		   =					fo.read()
	snake_case_   ,				snake_case_   ,				snake_case_		   =					content.split("_"							)
	snake_case_		   =					int(_A							)
	snake_case_		   =					int(_A							)

	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					,     _A							)							)

	snake_case_		   =					[]
	for block in encrypted_message.split(","							):
		encrypted_blocks.append(int(_A							)							)

	return decrypt_message(_A					,     _A					,     (n, d)					,     _A							)


def 						lowerCamelCase__   (		):







	'''simple docstring'''





	snake_case_		   =					"encrypted_file.txt"
	snake_case_		   =					input(R"Encrypt\Decrypt [e\d]: "							)

	if response.lower().startswith("e"							):
		snake_case_		   =					"encrypt"
	elif response.lower().startswith("d"							):
		snake_case_		   =					"decrypt"

	if mode == "encrypt":
		if not os.path.exists("rsa_pubkey.txt"							):
			rkg.make_key_files("rsa"					,     1024							)

		snake_case_		   =					input("\nEnter message: "							)
		snake_case_		   =					"rsa_pubkey.txt"
		print(f"Encrypting and writing to {filename}..."							)
		snake_case_		   =					encrypt_and_write_to_file(_A					,     _A					,     _A							)

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

	elif mode == "decrypt":
		snake_case_		   =					"rsa_privkey.txt"
		print(f"Reading from {filename} and decrypting..."							)
		snake_case_		   =					read_from_file_and_decrypt(_A					,     _A							)
		print("writing decryption to rsa_decryption.txt..."							)
		with open("rsa_decryption.txt"					,     "w"							) as dec:
			dec.write(_A							)

		print("\nDecryption:"							)
		print(_A							)


if __name__ == "__main__":
		main()






import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 		UpperCAmelCase       (    UpperCAmelCase__      ):







						'''simple docstring'''





						lowerCAmelCase_							=       (EulerAncestralDiscreteScheduler,)
						lowerCAmelCase_							=       10






						def 	snake_case__						(						self							:					Tuple	,     **__lowercase							:					int						):






							"""simple docstring"""






							snake_case_		   =					{
							    "num_train_timesteps": 11_00,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							}

							config.update(**__lowercase						)
							return config






						def 	snake_case__						(						self							:					Union[str, Any]						):






							"""simple docstring"""






							for timesteps in [10, 50, 1_00, 10_00]:
								self.check_over_configs(num_train_timesteps=__lowercase						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,     [0.0002, 0.002, 0.02]						):
								self.check_over_configs(beta_start=__lowercase	,     beta_end=__lowercase						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							for schedule in ["linear", "scaled_linear"]:
								self.check_over_configs(beta_schedule=__lowercase						)






						def 	snake_case__						(						self							:					Union[str, Any]						):






							"""simple docstring"""






							for prediction_type in ["epsilon", "v_prediction"]:
								self.check_over_configs(prediction_type=__lowercase						)






						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config()
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							snake_case_		   =					sample.to(__lowercase						)

							for i, t in enumerate(scheduler.timesteps						):
								snake_case_		   =					scheduler.scale_model_input(__lowercase	,     __lowercase						)

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(__lowercase						)						)
							snake_case_		   =					torch.mean(torch.abs(__lowercase						)						)

							assert abs(result_sum.item() - 152.3192						) < 1E-2
							assert abs(result_mean.item() - 0.1983						) < 1E-3






						def 	snake_case__						(						self							:					List[Any]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config(prediction_type="v_prediction"						)
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma
							snake_case_		   =					sample.to(__lowercase						)

							for i, t in enumerate(scheduler.timesteps						):
								snake_case_		   =					scheduler.scale_model_input(__lowercase	,     __lowercase						)

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(__lowercase						)						)
							snake_case_		   =					torch.mean(torch.abs(__lowercase						)						)

							assert abs(result_sum.item() - 108.4439						) < 1E-2
							assert abs(result_mean.item() - 0.1412						) < 1E-3






						def 	snake_case__						(						self							:					Optional[int]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config()
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps	,     device=__lowercase						)
							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							snake_case_		   =					sample.to(__lowercase						)

							for t in scheduler.timesteps:
								snake_case_		   =					scheduler.scale_model_input(__lowercase	,     __lowercase						)

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(__lowercase						)						)
							snake_case_		   =					torch.mean(torch.abs(__lowercase						)						)

							assert abs(result_sum.item() - 152.3192						) < 1E-2
							assert abs(result_mean.item() - 0.1983						) < 1E-3
