import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()


class _a       (	unittest.TestCase ):

	'''simple docstring'''



	lowerCamelCase_   :      Optional[Any]       =	"hf-internal-testing/tiny-stable-diffusion-pipe"
	lowerCamelCase_   :      Dict       =	1
	lowerCamelCase_   :      Optional[Any]       =	7_7
	lowerCamelCase_   :      int       =	3_2
	lowerCamelCase_   :      List[str]       =	4
	lowerCamelCase_   :      Any       =	6_4
	lowerCamelCase_   :      List[str]       =	torch.manual_seed(0 )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase=0.99_99   ):
			__A							:   List[Any]	  =				UNetaDConditionModel.from_pretrained(self.model_id							,			subfolder="unet"   )
			__A							:   Optional[Any]	  =				unet.to(__UpperCAmelCase   )
			__A							:   Tuple	  =				EMAModel(unet.parameters()							,			decay=__UpperCAmelCase							,			model_cls=__UpperCAmelCase							,			model_config=unet.config   )
			return unet, ema_unet

	def 					__UpperCAmelCase(       self   ):
			__A							:   int	  =				torch.randn(
			    self.batch_size							,			self.num_in_channels							,			self.latent_height							,			self.latent_width							,			generator=self.generator   ).to(__UpperCAmelCase   )
			__A							:   Optional[int]	  =				torch.randint(0							,			1_000							,			size=(self.batch_size,)							,			generator=self.generator   ).to(__UpperCAmelCase   )
			__A							:   List[str]	  =				torch.randn(
			    self.batch_size							,			self.prompt_length							,			self.text_encoder_hidden_dim							,			generator=self.generator   ).to(__UpperCAmelCase   )
			return noisy_latents, timesteps, encoder_hidden_states

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):
			__A							:   str	  =				{}
			for k, param in unet.state_dict().items():
					__A							:   List[str]	  =				torch.randn_like(__UpperCAmelCase   ) + (param * torch.randn_like(__UpperCAmelCase   ))
					updated_state_dict.update({k: updated_param}   )
			unet.load_state_dict(__UpperCAmelCase   )
			return unet

	def 					__UpperCAmelCase(       self   ):
			__A							:   str	  =				self.get_models()
			# Take the first (hypothetical) EMA step.
			ema_unet.step(unet.parameters()   )
			assert ema_unet.optimization_step == 1

			# Take two more.
			for _ in range(2   ):
					ema_unet.step(unet.parameters()   )
			assert ema_unet.optimization_step == 3

	def 					__UpperCAmelCase(       self   ):
			__A							:   Union[str, Any]	  =				self.get_models()
			# Since the `unet` is not being updated (i.e., backprop'd)
			# there won't be any difference between the `params` of `unet`
			# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
			ema_unet.step(unet.parameters()   )
			__A							:   List[str]	  =				list(unet.parameters()   )
			for s_param, param in zip(ema_unet.shadow_params							,			__UpperCAmelCase   ):
					assert torch.allclose(__UpperCAmelCase							,			__UpperCAmelCase   )

			# The above holds true even if we call `ema.step()` multiple times since
			# `unet` params are still not being updated.
			for _ in range(4   ):
					ema_unet.step(unet.parameters()   )
			for s_param, param in zip(ema_unet.shadow_params							,			__UpperCAmelCase   ):
					assert torch.allclose(__UpperCAmelCase							,			__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self   ):
			__A							:   str	  =				self.get_models()
			# Here we simulate the parameter updates for `unet`. Since there might
			# be some parameters which are initialized to zero we take extra care to
			# initialize their values to something non-zero before the multiplication.
			__A							:   Tuple	  =				self.simulate_backprop(__UpperCAmelCase   )

			# Take the EMA step.
			ema_unet.step(unet_pseudo_updated_step_one.parameters()   )

			# Now the EMA'd parameters won't be equal to the original model parameters.
			__A							:   Dict	  =				list(unet_pseudo_updated_step_one.parameters()   )
			for s_param, param in zip(ema_unet.shadow_params							,			__UpperCAmelCase   ):
					assert ~torch.allclose(__UpperCAmelCase							,			__UpperCAmelCase   )

			# Ensure this is the case when we take multiple EMA steps.
			for _ in range(4   ):
					ema_unet.step(unet.parameters()   )
			for s_param, param in zip(ema_unet.shadow_params							,			__UpperCAmelCase   ):
					assert ~torch.allclose(__UpperCAmelCase							,			__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self   ):
			# If we call EMA step after a backpropagation consecutively for two times,
			# the shadow params from those two steps should be different.
			__A							:   Dict	  =				self.get_models()

			# First backprop + EMA
			__A							:   List[Any]	  =				self.simulate_backprop(__UpperCAmelCase   )
			ema_unet.step(unet_step_one.parameters()   )
			__A							:   List[str]	  =				ema_unet.shadow_params

			# Second backprop + EMA
			__A							:   Union[str, Any]	  =				self.simulate_backprop(__UpperCAmelCase   )
			ema_unet.step(unet_step_two.parameters()   )
			__A							:   Tuple	  =				ema_unet.shadow_params

			for step_one, step_two in zip(__UpperCAmelCase							,			__UpperCAmelCase   ):
					assert ~torch.allclose(__UpperCAmelCase							,			__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self   ):
			# If there's no decay even if there are backprops, EMA steps
			# won't take any effect i.e., the shadow params would remain the
			# same.
			__A							:   Union[str, Any]	  =				self.get_models(decay=0.0   )
			__A							:   str	  =				self.simulate_backprop(__UpperCAmelCase   )
			ema_unet.step(unet_step_one.parameters()   )
			__A							:   str	  =				ema_unet.shadow_params

			__A							:   Any	  =				self.simulate_backprop(__UpperCAmelCase   )
			ema_unet.step(unet_step_two.parameters()   )
			__A							:   Optional[int]	  =				ema_unet.shadow_params

			for step_one, step_two in zip(__UpperCAmelCase							,			__UpperCAmelCase   ):
					assert torch.allclose(__UpperCAmelCase							,			__UpperCAmelCase   )







	@skip_mps
	def 					__UpperCAmelCase(       self   ):
			__A							:   Tuple	  =				self.get_models()
			__A							:   Optional[Any]	  =				self.get_dummy_inputs()

			with tempfile.TemporaryDirectory() as tmpdir:
					ema_unet.save_pretrained(__UpperCAmelCase   )
					__A							:   Optional[int]	  =				UNetaDConditionModel.from_pretrained(__UpperCAmelCase							,			model_cls=__UpperCAmelCase   )
					__A							:   Any	  =				loaded_unet.to(unet.device   )

			# Since no EMA step has been performed the outputs should match.
			__A							:   Any	  =				unet(__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase   ).sample
			__A							:   Any	  =				loaded_unet(__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase   ).sample

			assert torch.allclose(__UpperCAmelCase							,			__UpperCAmelCase							,			atol=1e-4   )






import os
import sys

from . import rsa_key_generator as rkg

UpperCamelCase      			=     128
UpperCamelCase      			=     256



def    lowerCamelCase_   (  _lowercase ,     _lowercase = DEFAULT_BLOCK_SIZE    )							->	list[int]:
		__A							:   List[str]	  =				message.encode("ascii"    )

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



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



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase = DEFAULT_BLOCK_SIZE    )							->	list[int]:
		__A							:   List[str]	  =				[]
		__A  ,       __A							:   List[Any]	  =				key

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



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase ,     _lowercase = DEFAULT_BLOCK_SIZE ,     )							->	str:
		__A							:   Optional[Any]	  =				[]
		__A  ,       __A							:   Dict	  =				key
		for block in encrypted_blocks:
				decrypted_blocks.append(pow(_lowercase ,     _lowercase ,     _lowercase    )    )
		return get_text_from_blocks(_lowercase ,     _lowercase ,     _lowercase    )



def    lowerCamelCase_   (  _lowercase    )							->	tuple[int, int, int]:
		with open(_lowercase    ) as fo:
				__A							:   Optional[int]	  =				fo.read()
		__A  ,       __A  ,       __A							:   Optional[int]	  =				content.split(","    )
		return (int(_lowercase    ), int(_lowercase    ), int(_lowercase    ))



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase ,     _lowercase = DEFAULT_BLOCK_SIZE ,     )							->	str:
		__A  ,       __A  ,       __A							:   Optional[int]	  =				read_key_file(_lowercase    )
		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 ,     _lowercase    )    )

		__A							:   Optional[int]	  =				[str(_lowercase    ) for i in encrypt_message(_lowercase ,     (n, e) ,     _lowercase    )]

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



def    lowerCamelCase_   (  _lowercase ,     _lowercase    )							->	str:
		__A  ,       __A  ,       __A							:   List[str]	  =				read_key_file(_lowercase    )
		with open(_lowercase    ) as fo:
				__A							:   Optional[Any]	  =				fo.read()
		__A  ,       __A  ,       __A							:   int	  =				content.split("_"    )
		__A							:   Tuple	  =				int(_lowercase    )
		__A							:   Any	  =				int(_lowercase    )

		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 ,     _lowercase    )    )

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

		return decrypt_message(_lowercase ,     _lowercase ,     (n, d) ,     _lowercase    )



def    lowerCamelCase_   (  )							->	None:
		__A							:   int	  =				"encrypted_file.txt"
		__A							:   int	  =				input(r"Encrypt\Decrypt [e\d]: "    )

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

		if mode == "encrypt":
				if not os.path.exists("rsa_pubkey.txt"    ):
						rkg.make_key_files("rsa" ,     1_024    )

				__A							:   Any	  =				input("\nEnter message: "    )
				__A							:   Any	  =				"rsa_pubkey.txt"
				print(F"Encrypting and writing to {filename}..."    )
				__A							:   List[str]	  =				encrypt_and_write_to_file(_lowercase ,     _lowercase ,     _lowercase    )

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

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

				print("\nDecryption:"    )
				print(_lowercase    )


if __name__ == "__main__":
					main()





